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Simultaneous genome editing, guide-donor <t>barcode</t> integration, and plasmid self-destruction (a) WT and nej1Δ were transformed with GAL-Cas9 and a guide-donor cassette to introduce a premature termination codon (PTC) in the ADE2 gene. Cas9 expression was induced by galactose and aliquots were harvested at the indicated generations. The ADE2 locus was analyzed by NGS and the fractions of WT sequence, NHEJ indels, and donor DNA-directed editing (either perfect or imperfect repair) were calculated (see Methods). The line graph shows the mean percentages at each generation from duplicate experiments. (b) Integration of the guide-donor barcode was assayed by amplification targeting the chromosomal barcode locus for the single ADE2 guide-donor plasmid (top) as well as a complex pool of > 100,000 barcoded guide-donor plasmids (bottom). The uncropped gel image indicates an absence of detectable NHEJ indel events at the barcode locus. Self-destruction of the guide-donor plasmids was assessed by a three-primer PCR, with a common forward primer and either a guide-donor plasmid-specific primer (top band) or a Cas9-plasmid specific primer (bottom band). (c) Cultures at the indicated generations of galactose induction were plated in quadruplicate at a density of ~1000 cells per plate on rich medium (YPD) and FCY1 counter-selectable medium (5-FC). The fraction of surviving colonies on plates are shown. All experiments were repeated with three biological replicates starting from independent transformations of the guide-donor plasmids.
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1) Product Images from "Multiplexed precision genome editing with trackable genomic barcodes in yeast"

Article Title: Multiplexed precision genome editing with trackable genomic barcodes in yeast

Journal: Nature biotechnology

doi: 10.1038/nbt.4137

Simultaneous genome editing, guide-donor barcode integration, and plasmid self-destruction (a) WT and nej1Δ were transformed with GAL-Cas9 and a guide-donor cassette to introduce a premature termination codon (PTC) in the ADE2 gene. Cas9 expression was induced by galactose and aliquots were harvested at the indicated generations. The ADE2 locus was analyzed by NGS and the fractions of WT sequence, NHEJ indels, and donor DNA-directed editing (either perfect or imperfect repair) were calculated (see Methods). The line graph shows the mean percentages at each generation from duplicate experiments. (b) Integration of the guide-donor barcode was assayed by amplification targeting the chromosomal barcode locus for the single ADE2 guide-donor plasmid (top) as well as a complex pool of > 100,000 barcoded guide-donor plasmids (bottom). The uncropped gel image indicates an absence of detectable NHEJ indel events at the barcode locus. Self-destruction of the guide-donor plasmids was assessed by a three-primer PCR, with a common forward primer and either a guide-donor plasmid-specific primer (top band) or a Cas9-plasmid specific primer (bottom band). (c) Cultures at the indicated generations of galactose induction were plated in quadruplicate at a density of ~1000 cells per plate on rich medium (YPD) and FCY1 counter-selectable medium (5-FC). The fraction of surviving colonies on plates are shown. All experiments were repeated with three biological replicates starting from independent transformations of the guide-donor plasmids.
Figure Legend Snippet: Simultaneous genome editing, guide-donor barcode integration, and plasmid self-destruction (a) WT and nej1Δ were transformed with GAL-Cas9 and a guide-donor cassette to introduce a premature termination codon (PTC) in the ADE2 gene. Cas9 expression was induced by galactose and aliquots were harvested at the indicated generations. The ADE2 locus was analyzed by NGS and the fractions of WT sequence, NHEJ indels, and donor DNA-directed editing (either perfect or imperfect repair) were calculated (see Methods). The line graph shows the mean percentages at each generation from duplicate experiments. (b) Integration of the guide-donor barcode was assayed by amplification targeting the chromosomal barcode locus for the single ADE2 guide-donor plasmid (top) as well as a complex pool of > 100,000 barcoded guide-donor plasmids (bottom). The uncropped gel image indicates an absence of detectable NHEJ indel events at the barcode locus. Self-destruction of the guide-donor plasmids was assessed by a three-primer PCR, with a common forward primer and either a guide-donor plasmid-specific primer (top band) or a Cas9-plasmid specific primer (bottom band). (c) Cultures at the indicated generations of galactose induction were plated in quadruplicate at a density of ~1000 cells per plate on rich medium (YPD) and FCY1 counter-selectable medium (5-FC). The fraction of surviving colonies on plates are shown. All experiments were repeated with three biological replicates starting from independent transformations of the guide-donor plasmids.

Techniques Used: Plasmid Preparation, Transformation Assay, Introduce, Expressing, Next-Generation Sequencing, Sequencing, Non-Homologous End Joining, Amplification, Polymerase Chain Reaction

The MAGESTIC pipeline for multiplexed precision genome editing (a) Linking guide-donors to short DNA barcodes. (1) A complex pool of array-synthesized oligonucleotides encoding guide-donors is amplified and cloned to generate the step 1 library (see Methods). The reverse primer introduces a semi-random 31-mer barcode into each ligation product, and NGS enables sequence validation and computational mapping of each guide-donor sequence in the step 1 library to a unique barcode. (b) Insertion of the Cas9 structural guide component plus yeast ( HIS3 ) and bacterial ( kanR ) selection markers in between the guide and donor. (1) This final step 2 library is transformed into yeast cells such that the vast majority of transformants uptake a single plasmid which accumulates to high-copy number. Each cell harbors a barcode integration locus with a counter-selectable marker ( FCY1 ). Guide-donor plasmids harbor a second guide expression unit (guide X) to promote barcode integration, as guide X cleavage sites flank FCY1 . Cas9 and guide expression results in simultaneous cleavage of the guide-donor plasmid at a guide X site adjacent to the downstream homology (DH), target site editing (right), and genomic integration of the guide-marker-donor-barcode cassette (left). (c) Library-scale genome editing and competitive growth phenotyping. (1) The guide-donor plasmids allow editing throughout the genome, while the barcode integration site is constant. (2) Pooled growth in different conditions results in enrichment or depletion of variants that affect fitness. (3) Variant fold-changes are calculated based on barcode sequencing counts in treated vs. untreated conditions.
Figure Legend Snippet: The MAGESTIC pipeline for multiplexed precision genome editing (a) Linking guide-donors to short DNA barcodes. (1) A complex pool of array-synthesized oligonucleotides encoding guide-donors is amplified and cloned to generate the step 1 library (see Methods). The reverse primer introduces a semi-random 31-mer barcode into each ligation product, and NGS enables sequence validation and computational mapping of each guide-donor sequence in the step 1 library to a unique barcode. (b) Insertion of the Cas9 structural guide component plus yeast ( HIS3 ) and bacterial ( kanR ) selection markers in between the guide and donor. (1) This final step 2 library is transformed into yeast cells such that the vast majority of transformants uptake a single plasmid which accumulates to high-copy number. Each cell harbors a barcode integration locus with a counter-selectable marker ( FCY1 ). Guide-donor plasmids harbor a second guide expression unit (guide X) to promote barcode integration, as guide X cleavage sites flank FCY1 . Cas9 and guide expression results in simultaneous cleavage of the guide-donor plasmid at a guide X site adjacent to the downstream homology (DH), target site editing (right), and genomic integration of the guide-marker-donor-barcode cassette (left). (c) Library-scale genome editing and competitive growth phenotyping. (1) The guide-donor plasmids allow editing throughout the genome, while the barcode integration site is constant. (2) Pooled growth in different conditions results in enrichment or depletion of variants that affect fitness. (3) Variant fold-changes are calculated based on barcode sequencing counts in treated vs. untreated conditions.

Techniques Used: Synthesized, Amplification, Clone Assay, Ligation, Next-Generation Sequencing, Sequencing, Selection, Transformation Assay, Plasmid Preparation, Marker, Expressing, Variant Assay

2) Product Images from "Quantitative Bias in Illumina TruSeq and a Novel Post Amplification Barcoding Strategy for Multiplexed DNA and Small RNA Deep Sequencing"

Article Title: Quantitative Bias in Illumina TruSeq and a Novel Post Amplification Barcoding Strategy for Multiplexed DNA and Small RNA Deep Sequencing

Journal: PLoS ONE

doi: 10.1371/journal.pone.0026969

Comparative schematic of small RNA barcoding methods. The three methods start with ligation of a 3' and 5' RNA adapter to generate a substrate for RT-PCR. In the pre-PCR barcoding method, the barcode is incorporated in the 5' adapter. In the TruSeq method, the barcode is incorporated in one of the RT-PCR primers. In the PALM barcoding method, the amplified RT-PCR product is A-tailed and ligated to a T-tailed barcoded adapter.
Figure Legend Snippet: Comparative schematic of small RNA barcoding methods. The three methods start with ligation of a 3' and 5' RNA adapter to generate a substrate for RT-PCR. In the pre-PCR barcoding method, the barcode is incorporated in the 5' adapter. In the TruSeq method, the barcode is incorporated in one of the RT-PCR primers. In the PALM barcoding method, the amplified RT-PCR product is A-tailed and ligated to a T-tailed barcoded adapter.

Techniques Used: Ligation, Reverse Transcription Polymerase Chain Reaction, Polymerase Chain Reaction, Amplification

3) Product Images from "Encoding Method of Single-cell Spatial Transcriptomics Sequencing"

Article Title: Encoding Method of Single-cell Spatial Transcriptomics Sequencing

Journal: International Journal of Biological Sciences

doi: 10.7150/ijbs.43887

Schematic of recent advances in single-cell spatial transcriptomics. A. Stahl et al. (2016) used a barcoded microplate with a diameter of 100 μm and a center-to-center distance of 200 μm, over an area of 6.2 mm by 6.6 mm. Stahl et al. (2018) used barcoded oligo-dT microarray slides divided into six subarrays, each with a size of 6.2 × 6.6 mm. Each subarray contains 1,007 circular spatial spots, each with a unique spatial barcode and an approximate diameter of 100 µm; the spots are also arranged with a center-to-center distance of 200 µm. Slide-seq (2019) used DNA-barcoded beads to reduce the spatial resolution to 10 µm. B. SPLiT-seq labeled transcriptomes with split-pool barcoding. In each split-pool round, fixed cells or nuclei are randomly distributed into wells, and transcripts are labeled with well-specific barcodes. Barcoded RT primers are used in the first round. Second- and third-round barcodes are appended to cDNA through ligation. A fourth barcode is added to cDNA molecules by PCR during sequencing library preparation. C. HDST deposits barcoded poly(d)T oligonucleotides into 2-μm wells with a randomly ordered bead array-based fabrication process and decodes their positions by a sequential hybridization and error-correcting strategy. Three rounds of split-and-pool were performed to produce a bead pool with 65×211×211 different oligonucleotide combinations. D. It shows the manner in which the DNA microscopy reaction encodes spatial location. Diffusing and amplifying clouds of UMI-tagged DNA overlap to extents that are determined by the proximity of their centers. UEIs between pairs of UMIs occur at frequencies determined by the degree of diffusion cloud overlap. E. DBiT-seq used two sets of barcodes A1-A50 and B1-B50 followed by ligation in situ yields a 2D mosaic of tissue pixels, each containing a unique combination of full barcode AiBj (i=1-50, j=1-50).
Figure Legend Snippet: Schematic of recent advances in single-cell spatial transcriptomics. A. Stahl et al. (2016) used a barcoded microplate with a diameter of 100 μm and a center-to-center distance of 200 μm, over an area of 6.2 mm by 6.6 mm. Stahl et al. (2018) used barcoded oligo-dT microarray slides divided into six subarrays, each with a size of 6.2 × 6.6 mm. Each subarray contains 1,007 circular spatial spots, each with a unique spatial barcode and an approximate diameter of 100 µm; the spots are also arranged with a center-to-center distance of 200 µm. Slide-seq (2019) used DNA-barcoded beads to reduce the spatial resolution to 10 µm. B. SPLiT-seq labeled transcriptomes with split-pool barcoding. In each split-pool round, fixed cells or nuclei are randomly distributed into wells, and transcripts are labeled with well-specific barcodes. Barcoded RT primers are used in the first round. Second- and third-round barcodes are appended to cDNA through ligation. A fourth barcode is added to cDNA molecules by PCR during sequencing library preparation. C. HDST deposits barcoded poly(d)T oligonucleotides into 2-μm wells with a randomly ordered bead array-based fabrication process and decodes their positions by a sequential hybridization and error-correcting strategy. Three rounds of split-and-pool were performed to produce a bead pool with 65×211×211 different oligonucleotide combinations. D. It shows the manner in which the DNA microscopy reaction encodes spatial location. Diffusing and amplifying clouds of UMI-tagged DNA overlap to extents that are determined by the proximity of their centers. UEIs between pairs of UMIs occur at frequencies determined by the degree of diffusion cloud overlap. E. DBiT-seq used two sets of barcodes A1-A50 and B1-B50 followed by ligation in situ yields a 2D mosaic of tissue pixels, each containing a unique combination of full barcode AiBj (i=1-50, j=1-50).

Techniques Used: Microarray, Labeling, Ligation, Polymerase Chain Reaction, Sequencing, Hybridization, Microscopy, Diffusion-based Assay, In Situ

4) Product Images from "A large accessory protein interactome is rewired across environments"

Article Title: A large accessory protein interactome is rewired across environments

Journal: bioRxiv

doi: 10.1101/2020.05.20.106583

Double barcodes and protein pairs in the PPiSeq library. (A) Distribution of the initial double barcode count of the PPiSeq library in SD environment at a sequencing depth of 209,899,687 reads. (B) Number of barcodes per protein pair in the PPiSeq library. Spike-in control protein pairs are not included in the plot.
Figure Legend Snippet: Double barcodes and protein pairs in the PPiSeq library. (A) Distribution of the initial double barcode count of the PPiSeq library in SD environment at a sequencing depth of 209,899,687 reads. (B) Number of barcodes per protein pair in the PPiSeq library. Spike-in control protein pairs are not included in the plot.

Techniques Used: Sequencing

PPiSeq (A) A cartoon of PPiSeq yeast library construction. Strains from the protein interactome collection are individually mated to strains from the double barcoder collection and sporulated to recover haploids that contain a mDHFR-tagged protein and a barcode. Haploids are mated as pools. In diploids, expression of Cre recombinase causes recombination between homologous chromosomes at the loxP locus, resulting in a contiguous double barcode that marks the mDHFR-tagged protein pair. (B) Representative double barcode frequency trajectories over twelve generations of competitive growth. Trajectories are used to calculate a quantitative fitness for each double barcoded strain. (C) Standard error of fitness estimates of protein pairs. The blue and red lines represent the median standard error for a sliding window (width = 0.05) of all fitness-ranked protein pairs and of only the positive protein-protein interactions, respectively. (D) Estimated fitness of strains with different double barcodes representing the same protein pair in the same pooled growth. Positive protein pairs are randomly selected within a fitness window. ORF x Null is a violin plot of the fitness distribution of all interactions with a mDHFR fragment that is not tethered to a yeast protein. DHFR(-) is yeast strains that lack any mDHFR fragment. DHFR(+) is yeast strains that contain a full length mDHFR under a strong promoter. (E) Density plot of the fitness of double barcodes that represent the same putative PPI in the same pooled growth. In B-E , the data in SD environment are used. (F) Density plot of the normalized mean fitness of the same PPI between two pooled growth cultures in SD environment. PPIs detected in either one growth culture are included. (G) Venn diagram of the number of PPIs identified within our search space by PPiSeq in 9 environments (magenta), PPiSeq in SD environment (pink), the interactome-scale protein-fragment complementation screen (PCA, yellow), and the BioGRID database excluding any PPIs previously detected by PCA (blue).
Figure Legend Snippet: PPiSeq (A) A cartoon of PPiSeq yeast library construction. Strains from the protein interactome collection are individually mated to strains from the double barcoder collection and sporulated to recover haploids that contain a mDHFR-tagged protein and a barcode. Haploids are mated as pools. In diploids, expression of Cre recombinase causes recombination between homologous chromosomes at the loxP locus, resulting in a contiguous double barcode that marks the mDHFR-tagged protein pair. (B) Representative double barcode frequency trajectories over twelve generations of competitive growth. Trajectories are used to calculate a quantitative fitness for each double barcoded strain. (C) Standard error of fitness estimates of protein pairs. The blue and red lines represent the median standard error for a sliding window (width = 0.05) of all fitness-ranked protein pairs and of only the positive protein-protein interactions, respectively. (D) Estimated fitness of strains with different double barcodes representing the same protein pair in the same pooled growth. Positive protein pairs are randomly selected within a fitness window. ORF x Null is a violin plot of the fitness distribution of all interactions with a mDHFR fragment that is not tethered to a yeast protein. DHFR(-) is yeast strains that lack any mDHFR fragment. DHFR(+) is yeast strains that contain a full length mDHFR under a strong promoter. (E) Density plot of the fitness of double barcodes that represent the same putative PPI in the same pooled growth. In B-E , the data in SD environment are used. (F) Density plot of the normalized mean fitness of the same PPI between two pooled growth cultures in SD environment. PPIs detected in either one growth culture are included. (G) Venn diagram of the number of PPIs identified within our search space by PPiSeq in 9 environments (magenta), PPiSeq in SD environment (pink), the interactome-scale protein-fragment complementation screen (PCA, yellow), and the BioGRID database excluding any PPIs previously detected by PCA (blue).

Techniques Used: Expressing

5) Product Images from "Cost-effective, high-throughput DNA sequencing libraries for multiplexed target capture"

Article Title: Cost-effective, high-throughput DNA sequencing libraries for multiplexed target capture

Journal: Genome Research

doi: 10.1101/gr.128124.111

( A ) Schematic overview of the library preparation procedure using the Illumina PE adapter (internal barcode in red). After a cascade of enzymatic reactions and cleanup steps, enrichment PCR can be performed to complete the adapter sites for Illumina PE
Figure Legend Snippet: ( A ) Schematic overview of the library preparation procedure using the Illumina PE adapter (internal barcode in red). After a cascade of enzymatic reactions and cleanup steps, enrichment PCR can be performed to complete the adapter sites for Illumina PE

Techniques Used: Polymerase Chain Reaction

6) Product Images from "WILD-seq: Clonal deconvolution of transcriptomic signatures of sensitivity and resistance to cancer therapeutics in vivo"

Article Title: WILD-seq: Clonal deconvolution of transcriptomic signatures of sensitivity and resistance to cancer therapeutics in vivo

Journal: bioRxiv

doi: 10.1101/2021.12.09.471927

Simultaneous detection of changes in clonal abundance, gene expression, and tumour microenvironment in response to BET bromodomain inhibition with WILD-seq. a. Tumour growth curves with JQ1 treatment. 4T1 WILD-seq tumours were treated with the BET bromodomain inhibitor JQ1 or vehicle from 7 days post-implantation until endpoint (n = 4 mice per condition). Data represents mean ± SEM. b. scRNA-seq of JQ1-treated 4T1 WILD-seq tumours. UMAP plots of vehicle- or JQ1-treated 4T1 WILD-seq tumours. Combined cells from 2 independent experiments, each with 3 mice per condition are shown. Cells for which a WILD-seq clonal barcode is identified are shown as dark grey or coloured spots. Cells which belong to four selected clonal lineages are highlighted. c. JQ1-treatment results in a reduction in Cd8+ tumour-associated T-cells. Cells belonging to the T- cell compartment were computationally extracted from the single cell data and reclustered. Upper panels show combined UMAP plots from experiments A and B with Cd8a expression per cell illustrated enabling identification of the Cd8+ T cell cluster. Lower panels show neighborhood graphs of the results from differential abundance testing using Milo 16 . Coloured nodes represent neighbourhoods with significantly different cell numbers between conditions (FDR
Figure Legend Snippet: Simultaneous detection of changes in clonal abundance, gene expression, and tumour microenvironment in response to BET bromodomain inhibition with WILD-seq. a. Tumour growth curves with JQ1 treatment. 4T1 WILD-seq tumours were treated with the BET bromodomain inhibitor JQ1 or vehicle from 7 days post-implantation until endpoint (n = 4 mice per condition). Data represents mean ± SEM. b. scRNA-seq of JQ1-treated 4T1 WILD-seq tumours. UMAP plots of vehicle- or JQ1-treated 4T1 WILD-seq tumours. Combined cells from 2 independent experiments, each with 3 mice per condition are shown. Cells for which a WILD-seq clonal barcode is identified are shown as dark grey or coloured spots. Cells which belong to four selected clonal lineages are highlighted. c. JQ1-treatment results in a reduction in Cd8+ tumour-associated T-cells. Cells belonging to the T- cell compartment were computationally extracted from the single cell data and reclustered. Upper panels show combined UMAP plots from experiments A and B with Cd8a expression per cell illustrated enabling identification of the Cd8+ T cell cluster. Lower panels show neighborhood graphs of the results from differential abundance testing using Milo 16 . Coloured nodes represent neighbourhoods with significantly different cell numbers between conditions (FDR

Techniques Used: Expressing, Inhibition, Mouse Assay

Clonal transcriptomic correlates of response and resistance to taxane chemotherapy in the 4T1 mammary carcinoma model. a. Tumour growth curves with docetaxel treatment. 4T1 WILD-seq tumours were treated with docetaxel or vehicle (12.5% ethanol, 12.5% Kolliphor) from 7 days post-implantation for 2 weeks (n = 5 mice per condition). Dosing regimen was 12.5 mg/Kg docetaxel three times per week. Data represents mean ± SEM. b. scRNA-seq of docetaxel-treated 4T1 WILD- seq tumours. UMAP plots of vehicle- or docetaxel-treated 4T1 WILD-seq tumours. Combined cells from 2 independent experiments, each with 3 mice per condition are shown. Cells for which a WILD- seq clonal barcode is identified are shown as dark grey or coloured spots. Cells which belong to three selected clonal lineages are highlighted. c. Clonal representation. Proportion of tumour cells assigned to each clonal lineage in experiment C based on the WILD-seq barcode (n = 3 tumours per condition). Clones which make up at least 2% of the assigned tumour cells under at least one condition are plotted. The most sensitive clone 238 is highlighted in blue and the most resistant clone 679 is highlighted in red. Data represents mean ± SEM. d. Clonal response to docetaxel-treatment. Log 2 fold change in clonal proportions upon docetaxel treatment across experiments C and D. Fold change was calculated by comparing each docetaxel-treated sample with the mean of the 3 corresponding vehicle-treated samples from the same experiment. p-values calculated by one-sample t-test vs a theoretical mean of 0. Data represents mean ± SEM. e. and f. Correlation of docetaxel-response with baseline clonal transcriptomic signatures. Clonal gene set enrichment scores for vehicle-treated tumours were calculated by comparing cells of a specific clonal lineage of interest to all assigned tumour cells within the same experiment. Correlation between these scores and docetaxel-treatment response (mean log 2 fold change clonal proportion docetaxel vs vehicle) was then calculated for each gene set. Selected gene sets with the highest positive or negative correlation values (Pearson correlation test) are shown. A positive correlation indicates a higher expression in resistant clones, whereas a negative correlation indicates a higher expression in sensitive clones.
Figure Legend Snippet: Clonal transcriptomic correlates of response and resistance to taxane chemotherapy in the 4T1 mammary carcinoma model. a. Tumour growth curves with docetaxel treatment. 4T1 WILD-seq tumours were treated with docetaxel or vehicle (12.5% ethanol, 12.5% Kolliphor) from 7 days post-implantation for 2 weeks (n = 5 mice per condition). Dosing regimen was 12.5 mg/Kg docetaxel three times per week. Data represents mean ± SEM. b. scRNA-seq of docetaxel-treated 4T1 WILD- seq tumours. UMAP plots of vehicle- or docetaxel-treated 4T1 WILD-seq tumours. Combined cells from 2 independent experiments, each with 3 mice per condition are shown. Cells for which a WILD- seq clonal barcode is identified are shown as dark grey or coloured spots. Cells which belong to three selected clonal lineages are highlighted. c. Clonal representation. Proportion of tumour cells assigned to each clonal lineage in experiment C based on the WILD-seq barcode (n = 3 tumours per condition). Clones which make up at least 2% of the assigned tumour cells under at least one condition are plotted. The most sensitive clone 238 is highlighted in blue and the most resistant clone 679 is highlighted in red. Data represents mean ± SEM. d. Clonal response to docetaxel-treatment. Log 2 fold change in clonal proportions upon docetaxel treatment across experiments C and D. Fold change was calculated by comparing each docetaxel-treated sample with the mean of the 3 corresponding vehicle-treated samples from the same experiment. p-values calculated by one-sample t-test vs a theoretical mean of 0. Data represents mean ± SEM. e. and f. Correlation of docetaxel-response with baseline clonal transcriptomic signatures. Clonal gene set enrichment scores for vehicle-treated tumours were calculated by comparing cells of a specific clonal lineage of interest to all assigned tumour cells within the same experiment. Correlation between these scores and docetaxel-treatment response (mean log 2 fold change clonal proportion docetaxel vs vehicle) was then calculated for each gene set. Selected gene sets with the highest positive or negative correlation values (Pearson correlation test) are shown. A positive correlation indicates a higher expression in resistant clones, whereas a negative correlation indicates a higher expression in sensitive clones.

Techniques Used: Mouse Assay, Clone Assay, Expressing

Clonal transcriptomic signatures of response and resistance to taxane chemotherapy in the D2A1 mammary carcinoma model. a. D2A1 WILD-seq tumour growth curves with docetaxel treatment. D2A1 WILD-seq tumours were treated with docetaxel or vehicle from 7 days post-implantation for 2 weeks (n = 5 vehicle-treated mice, n = 4 docetaxel-treated mice). Data represents mean ± SEM. b. scRNA-seq of docetaxel-treated D2A1 WILD-seq tumours. UMAP plots of vehicle-treated D2A1 WILD-seq D2A1 tumours and reclustered barcoded-tumour cells from vehicle- and docetaxel-treated tumours. Combined cells from 3 mice per condition are shown. Cells for which a WILD-seq clonal barcode is identified are shown as dark grey or coloured spots. Cells which belong to five selected clonal lineages are highlighted. c. Comparison of EMT status of major 4T1 and D2A1 WILD-seq clones. Violin plot of AUCell scores from vehicle-treated tumour cells generated using the HOLLERN_EMT_BREAST_TUMOR_DN 54 gene set, a set of genes that have low expression in murine mammary tumours of mesenchymal histology. 4T1 WILD-seq clones exhibit varying levels of expression of this geneset whereas D2A1 WILD-seq clones have consistently low levels of expression of these genes. d. Clonal representation. Proportion of tumour cells assigned to each clonal lineage based on the WILD-seq barcode (n = 3 tumours per condition). Clones which make up at least 2% of the assigned tumour cells under at least one condition are plotted. The most sensitive clones to docetaxel treatment 118, 2874 and 1072 are highlighted in blue and the most resistant clones 1240, 1197 and 751 are highlighted in red. Data represents mean ± SEM. e. Clonal transcriptomic signatures from vehicle-treated tumours. Heatmap of median AUCell scores per sample for each of the five most abundant clones. All gene sets which showed consistent and statistically significant enrichment (combined fisher p-value
Figure Legend Snippet: Clonal transcriptomic signatures of response and resistance to taxane chemotherapy in the D2A1 mammary carcinoma model. a. D2A1 WILD-seq tumour growth curves with docetaxel treatment. D2A1 WILD-seq tumours were treated with docetaxel or vehicle from 7 days post-implantation for 2 weeks (n = 5 vehicle-treated mice, n = 4 docetaxel-treated mice). Data represents mean ± SEM. b. scRNA-seq of docetaxel-treated D2A1 WILD-seq tumours. UMAP plots of vehicle-treated D2A1 WILD-seq D2A1 tumours and reclustered barcoded-tumour cells from vehicle- and docetaxel-treated tumours. Combined cells from 3 mice per condition are shown. Cells for which a WILD-seq clonal barcode is identified are shown as dark grey or coloured spots. Cells which belong to five selected clonal lineages are highlighted. c. Comparison of EMT status of major 4T1 and D2A1 WILD-seq clones. Violin plot of AUCell scores from vehicle-treated tumour cells generated using the HOLLERN_EMT_BREAST_TUMOR_DN 54 gene set, a set of genes that have low expression in murine mammary tumours of mesenchymal histology. 4T1 WILD-seq clones exhibit varying levels of expression of this geneset whereas D2A1 WILD-seq clones have consistently low levels of expression of these genes. d. Clonal representation. Proportion of tumour cells assigned to each clonal lineage based on the WILD-seq barcode (n = 3 tumours per condition). Clones which make up at least 2% of the assigned tumour cells under at least one condition are plotted. The most sensitive clones to docetaxel treatment 118, 2874 and 1072 are highlighted in blue and the most resistant clones 1240, 1197 and 751 are highlighted in red. Data represents mean ± SEM. e. Clonal transcriptomic signatures from vehicle-treated tumours. Heatmap of median AUCell scores per sample for each of the five most abundant clones. All gene sets which showed consistent and statistically significant enrichment (combined fisher p-value

Techniques Used: Mouse Assay, Clone Assay, Generated, Expressing

Establishment of an expressed barcode system to simultaneously detect clonal lineage and gene expression from single cells in vivo . a. Lentiviral construct design. A PGK promoter drives expression of a transcript encoding zsGreen harbouring a WILD-seq barcode sequence in the 3’UTR. A spacer sequence and polyadenylation signal ensure that that the barcode is detectable as part of a standard oligo dT single cell RNA library preparation and sequencing pipeline. The barcode cassette comprises 2 distinct 12 nucleotide barcode sequences separated by a constant 20 nucleotide linker region. The library of barcode sequences was designed with Hamming distance 5 to allow for sequencing error correction. b. Schematic of WILD-seq method. Tumour cells are infected with the WILD-seq lentiviral library and an appropriate size population of zsGreen positive cells isolated, each of which will express a single unique WILD-seq barcode. This WILD-seq barcoded, heterogenous cell pool is then subjected to an intervention of interest (such as in vivo treatment of the implanted pool with a therapeutic agent) and subsequently analysed by single cell RNA sequencing using the 10X Genomics platform. An additional PCR amplification step is included that specifically enriches for the barcode sequence to increase the number of cells to which a WILD-seq barcode can be conclusively assigned. c. scRNA-seq of in vitro 4T1 WILD-seq cell pool. UMAP plot of in vitro cultured 4T1 WILD-seq cells. Cells for which a WILD-seq clonal barcode is identified are shown as dark grey or coloured spots. Cells which belong to five selected clonal lineages are highlighted. d. scRNA-seq of 4T1 WILD-seq tumours. UMAP plots of vehicle-treated 4T1 WILD-seq tumours generated by injecting the 4T1 WILD-seq pool into the mammary fatpad of BALB/c mice. Four independent experiments were performed each involving injection into 3 separate host animals. Six animals from experiments A and B received vehicle 1 (10% DMSO, 0.9%β- cyclodextrin) and six animals from experiments C and D received vehicle 2 (12.5%ethanol, 12.5% Kolliphor). e. Clonal representation. Proportion of tumour cells assigned to each clonal lineage based on the WILD-seq barcode (n = 1 for in vitro cultured cells, n = 6 for tumours from NSG mice, n = 12 for vehicle-treated tumours from BALB/c mice). Selected clones from the most abundant lineages are plotted. Data represents mean ± SEM. f. Principal component analysis of clonal transcriptomes. Pseudo-bulk analysis was performed by summing counts for all tumour cells expressing the same WILD-seq clonal barcode within an independent experiment. For in vivo tumour samples each point represents the combined cells from 3 animals. Principal component analysis of normalized pseudo-bulk count data showed separation of samples by origin with PC1 and PC2 and separation by clonality with PC3. g. Transcriptomic programs associated with principal components. The top/bottom 50 gene loadings of PC1, PC2 and PC3 were analysed using Enrichr 51 – 53 . h. Clonal transcriptomic signatures from vehicle-treated BALB/c tumours. An AUCell score 14 enrichment was calculated for each clone and for each experiment by comparing cells of a specific clonal lineage of interest to all assigned tumour cells within the same experiment. All gene sets which showed consistent and statistically significant enrichment in one of the six most abundant clones across experiments are illustrated.
Figure Legend Snippet: Establishment of an expressed barcode system to simultaneously detect clonal lineage and gene expression from single cells in vivo . a. Lentiviral construct design. A PGK promoter drives expression of a transcript encoding zsGreen harbouring a WILD-seq barcode sequence in the 3’UTR. A spacer sequence and polyadenylation signal ensure that that the barcode is detectable as part of a standard oligo dT single cell RNA library preparation and sequencing pipeline. The barcode cassette comprises 2 distinct 12 nucleotide barcode sequences separated by a constant 20 nucleotide linker region. The library of barcode sequences was designed with Hamming distance 5 to allow for sequencing error correction. b. Schematic of WILD-seq method. Tumour cells are infected with the WILD-seq lentiviral library and an appropriate size population of zsGreen positive cells isolated, each of which will express a single unique WILD-seq barcode. This WILD-seq barcoded, heterogenous cell pool is then subjected to an intervention of interest (such as in vivo treatment of the implanted pool with a therapeutic agent) and subsequently analysed by single cell RNA sequencing using the 10X Genomics platform. An additional PCR amplification step is included that specifically enriches for the barcode sequence to increase the number of cells to which a WILD-seq barcode can be conclusively assigned. c. scRNA-seq of in vitro 4T1 WILD-seq cell pool. UMAP plot of in vitro cultured 4T1 WILD-seq cells. Cells for which a WILD-seq clonal barcode is identified are shown as dark grey or coloured spots. Cells which belong to five selected clonal lineages are highlighted. d. scRNA-seq of 4T1 WILD-seq tumours. UMAP plots of vehicle-treated 4T1 WILD-seq tumours generated by injecting the 4T1 WILD-seq pool into the mammary fatpad of BALB/c mice. Four independent experiments were performed each involving injection into 3 separate host animals. Six animals from experiments A and B received vehicle 1 (10% DMSO, 0.9%β- cyclodextrin) and six animals from experiments C and D received vehicle 2 (12.5%ethanol, 12.5% Kolliphor). e. Clonal representation. Proportion of tumour cells assigned to each clonal lineage based on the WILD-seq barcode (n = 1 for in vitro cultured cells, n = 6 for tumours from NSG mice, n = 12 for vehicle-treated tumours from BALB/c mice). Selected clones from the most abundant lineages are plotted. Data represents mean ± SEM. f. Principal component analysis of clonal transcriptomes. Pseudo-bulk analysis was performed by summing counts for all tumour cells expressing the same WILD-seq clonal barcode within an independent experiment. For in vivo tumour samples each point represents the combined cells from 3 animals. Principal component analysis of normalized pseudo-bulk count data showed separation of samples by origin with PC1 and PC2 and separation by clonality with PC3. g. Transcriptomic programs associated with principal components. The top/bottom 50 gene loadings of PC1, PC2 and PC3 were analysed using Enrichr 51 – 53 . h. Clonal transcriptomic signatures from vehicle-treated BALB/c tumours. An AUCell score 14 enrichment was calculated for each clone and for each experiment by comparing cells of a specific clonal lineage of interest to all assigned tumour cells within the same experiment. All gene sets which showed consistent and statistically significant enrichment in one of the six most abundant clones across experiments are illustrated.

Techniques Used: Expressing, In Vivo, Construct, Sequencing, Infection, Isolation, RNA Sequencing Assay, Polymerase Chain Reaction, Amplification, In Vitro, Cell Culture, Generated, Mouse Assay, Injection, Clone Assay

7) Product Images from "UMI-Reducer: Collapsing duplicate sequencing reads via Unique Molecular Identifiers"

Article Title: UMI-Reducer: Collapsing duplicate sequencing reads via Unique Molecular Identifiers

Journal: bioRxiv

doi: 10.1101/103267

Overview of the UMI-Reducer. (1) A schematic of 150 bp sequencing read, obtained by Illumina MiSeq Illumina V3 150 cycle kit. Read is comprised of the following parts: barcode (blue color, 4bp), Unique Molecular Identifiers (UMI) (red color, 7 bp), degenerate sequence (yellow color, 3bp), mRNA fragment to be sequenced (hereafter referred to as “read”) (green color, variable length), L32 RNA linker (grey color, GTGTCAGTCACTTCCAGCGG, 20bp), and Illumina adaptor (grey color, CCGCTGGAAGTGACTGACAC). The degenerate sequence represents the amino acids A, G, or T (but not C) in a degenerate region of three positions. This sequence is designed to reduce ligation bias during library preparation, as the CircLigase enzyme does not prefer the base C. (2) UMI-Reducer steps. Raw sequencing data prepared as described in (1) enters the pipeline (2a). Reads containing the full sequence of L32 RNA linker are identified, and the RNA sequence is extracted and saved into a FASTQ file. Corresponding UMI sequences are added to the read ID and used in downstream analyses. Corresponding barcode is used to partition reads into individual samples (not shown). Reads are then mapped to the genome using tophat2 (2b). Reads with matching UMIs that are located in identical positions along the genome (i.e., identical start and end position of the read) are considered technical duplicates; reads with different UMIs that are located in the same position are considered biological duplicates. UMI-Reducer collapses technical duplicates by combining all copies into one read (2c). UMI-Reducer reports, as a pie chart, the genomic profiles of mapped reads after filtering out multi-mapped reads and collapsing PCR duplicates. The genome profiler annotates reads into the following categories (2d): junction reads, CDS, 5’UTR, 3’UTR, intron, intergenic, deep-intergenic (reads mapped 1Kb away from the gene boundaries), and mitochondrial reads (MT). UMI-Reducer reports, as a histogram, length distribution of uniquely mapped reads after collapsing technical duplicates.
Figure Legend Snippet: Overview of the UMI-Reducer. (1) A schematic of 150 bp sequencing read, obtained by Illumina MiSeq Illumina V3 150 cycle kit. Read is comprised of the following parts: barcode (blue color, 4bp), Unique Molecular Identifiers (UMI) (red color, 7 bp), degenerate sequence (yellow color, 3bp), mRNA fragment to be sequenced (hereafter referred to as “read”) (green color, variable length), L32 RNA linker (grey color, GTGTCAGTCACTTCCAGCGG, 20bp), and Illumina adaptor (grey color, CCGCTGGAAGTGACTGACAC). The degenerate sequence represents the amino acids A, G, or T (but not C) in a degenerate region of three positions. This sequence is designed to reduce ligation bias during library preparation, as the CircLigase enzyme does not prefer the base C. (2) UMI-Reducer steps. Raw sequencing data prepared as described in (1) enters the pipeline (2a). Reads containing the full sequence of L32 RNA linker are identified, and the RNA sequence is extracted and saved into a FASTQ file. Corresponding UMI sequences are added to the read ID and used in downstream analyses. Corresponding barcode is used to partition reads into individual samples (not shown). Reads are then mapped to the genome using tophat2 (2b). Reads with matching UMIs that are located in identical positions along the genome (i.e., identical start and end position of the read) are considered technical duplicates; reads with different UMIs that are located in the same position are considered biological duplicates. UMI-Reducer collapses technical duplicates by combining all copies into one read (2c). UMI-Reducer reports, as a pie chart, the genomic profiles of mapped reads after filtering out multi-mapped reads and collapsing PCR duplicates. The genome profiler annotates reads into the following categories (2d): junction reads, CDS, 5’UTR, 3’UTR, intron, intergenic, deep-intergenic (reads mapped 1Kb away from the gene boundaries), and mitochondrial reads (MT). UMI-Reducer reports, as a histogram, length distribution of uniquely mapped reads after collapsing technical duplicates.

Techniques Used: Sequencing, Ligation, Polymerase Chain Reaction

8) Product Images from "Multiplexed precision genome editing with trackable genomic barcodes in yeast"

Article Title: Multiplexed precision genome editing with trackable genomic barcodes in yeast

Journal: Nature biotechnology

doi: 10.1038/nbt.4137

Simultaneous genome editing, guide-donor barcode integration, and plasmid self-destruction (a) WT and nej1Δ were transformed with GAL-Cas9 and a guide-donor cassette to introduce a premature termination codon (PTC) in the ADE2 gene. Cas9 expression was induced by galactose and aliquots were harvested at the indicated generations. The ADE2 locus was analyzed by NGS and the fractions of WT sequence, NHEJ indels, and donor DNA-directed editing (either perfect or imperfect repair) were calculated (see Methods). The line graph shows the mean percentages at each generation from duplicate experiments. (b) Integration of the guide-donor barcode was assayed by amplification targeting the chromosomal barcode locus for the single ADE2 guide-donor plasmid (top) as well as a complex pool of > 100,000 barcoded guide-donor plasmids (bottom). The uncropped gel image indicates an absence of detectable NHEJ indel events at the barcode locus. Self-destruction of the guide-donor plasmids was assessed by a three-primer PCR, with a common forward primer and either a guide-donor plasmid-specific primer (top band) or a Cas9-plasmid specific primer (bottom band). (c) Cultures at the indicated generations of galactose induction were plated in quadruplicate at a density of ~1000 cells per plate on rich medium (YPD) and FCY1 counter-selectable medium (5-FC). The fraction of surviving colonies on plates are shown. All experiments were repeated with three biological replicates starting from independent transformations of the guide-donor plasmids.
Figure Legend Snippet: Simultaneous genome editing, guide-donor barcode integration, and plasmid self-destruction (a) WT and nej1Δ were transformed with GAL-Cas9 and a guide-donor cassette to introduce a premature termination codon (PTC) in the ADE2 gene. Cas9 expression was induced by galactose and aliquots were harvested at the indicated generations. The ADE2 locus was analyzed by NGS and the fractions of WT sequence, NHEJ indels, and donor DNA-directed editing (either perfect or imperfect repair) were calculated (see Methods). The line graph shows the mean percentages at each generation from duplicate experiments. (b) Integration of the guide-donor barcode was assayed by amplification targeting the chromosomal barcode locus for the single ADE2 guide-donor plasmid (top) as well as a complex pool of > 100,000 barcoded guide-donor plasmids (bottom). The uncropped gel image indicates an absence of detectable NHEJ indel events at the barcode locus. Self-destruction of the guide-donor plasmids was assessed by a three-primer PCR, with a common forward primer and either a guide-donor plasmid-specific primer (top band) or a Cas9-plasmid specific primer (bottom band). (c) Cultures at the indicated generations of galactose induction were plated in quadruplicate at a density of ~1000 cells per plate on rich medium (YPD) and FCY1 counter-selectable medium (5-FC). The fraction of surviving colonies on plates are shown. All experiments were repeated with three biological replicates starting from independent transformations of the guide-donor plasmids.

Techniques Used: Plasmid Preparation, Transformation Assay, Introduce, Expressing, Next-Generation Sequencing, Sequencing, Non-Homologous End Joining, Amplification, Polymerase Chain Reaction

The MAGESTIC pipeline for multiplexed precision genome editing (a) Linking guide-donors to short DNA barcodes. (1) A complex pool of array-synthesized oligonucleotides encoding guide-donors is amplified and cloned to generate the step 1 library (see Methods). The reverse primer introduces a semi-random 31-mer barcode into each ligation product, and NGS enables sequence validation and computational mapping of each guide-donor sequence in the step 1 library to a unique barcode. (b) Insertion of the Cas9 structural guide component plus yeast ( HIS3 ) and bacterial ( kanR ) selection markers in between the guide and donor. (1) This final step 2 library is transformed into yeast cells such that the vast majority of transformants uptake a single plasmid which accumulates to high-copy number. Each cell harbors a barcode integration locus with a counter-selectable marker ( FCY1 ). Guide-donor plasmids harbor a second guide expression unit (guide X) to promote barcode integration, as guide X cleavage sites flank FCY1 . Cas9 and guide expression results in simultaneous cleavage of the guide-donor plasmid at a guide X site adjacent to the downstream homology (DH), target site editing (right), and genomic integration of the guide-marker-donor-barcode cassette (left). (c) Library-scale genome editing and competitive growth phenotyping. (1) The guide-donor plasmids allow editing throughout the genome, while the barcode integration site is constant. (2) Pooled growth in different conditions results in enrichment or depletion of variants that affect fitness. (3) Variant fold-changes are calculated based on barcode sequencing counts in treated vs. untreated conditions.
Figure Legend Snippet: The MAGESTIC pipeline for multiplexed precision genome editing (a) Linking guide-donors to short DNA barcodes. (1) A complex pool of array-synthesized oligonucleotides encoding guide-donors is amplified and cloned to generate the step 1 library (see Methods). The reverse primer introduces a semi-random 31-mer barcode into each ligation product, and NGS enables sequence validation and computational mapping of each guide-donor sequence in the step 1 library to a unique barcode. (b) Insertion of the Cas9 structural guide component plus yeast ( HIS3 ) and bacterial ( kanR ) selection markers in between the guide and donor. (1) This final step 2 library is transformed into yeast cells such that the vast majority of transformants uptake a single plasmid which accumulates to high-copy number. Each cell harbors a barcode integration locus with a counter-selectable marker ( FCY1 ). Guide-donor plasmids harbor a second guide expression unit (guide X) to promote barcode integration, as guide X cleavage sites flank FCY1 . Cas9 and guide expression results in simultaneous cleavage of the guide-donor plasmid at a guide X site adjacent to the downstream homology (DH), target site editing (right), and genomic integration of the guide-marker-donor-barcode cassette (left). (c) Library-scale genome editing and competitive growth phenotyping. (1) The guide-donor plasmids allow editing throughout the genome, while the barcode integration site is constant. (2) Pooled growth in different conditions results in enrichment or depletion of variants that affect fitness. (3) Variant fold-changes are calculated based on barcode sequencing counts in treated vs. untreated conditions.

Techniques Used: Synthesized, Amplification, Clone Assay, Ligation, Next-Generation Sequencing, Sequencing, Selection, Transformation Assay, Plasmid Preparation, Marker, Expressing, Variant Assay

9) Product Images from "Meiotic, genomic and evolutionary properties of crossover distribution in Drosophila yakuba"

Article Title: Meiotic, genomic and evolutionary properties of crossover distribution in Drosophila yakuba

Journal: PLoS Genetics

doi: 10.1371/journal.pgen.1010087

A high-resolution crossover map in D . yakuba using dual SNP-barcode genotyping
Figure Legend Snippet: A high-resolution crossover map in D . yakuba using dual SNP-barcode genotyping

Techniques Used: Genotyping Assay

Dual-barcoding genotyping method used to obtain crossover rates. Diagnostic SNPs are used as genetic barcodes and allow the pooling of multiple F 2 individuals from different crosses for a given sequence barcode (left panel). The combination of genetic and sequence barcodes ensures efficient genotyping of multiple individuals and accurate crossover localization along chromosome arms (right panel). Diagnostic, or strain-specific SNPs, are singletons for the complete set of genotypes used in the study, including the tester line.
Figure Legend Snippet: Dual-barcoding genotyping method used to obtain crossover rates. Diagnostic SNPs are used as genetic barcodes and allow the pooling of multiple F 2 individuals from different crosses for a given sequence barcode (left panel). The combination of genetic and sequence barcodes ensures efficient genotyping of multiple individuals and accurate crossover localization along chromosome arms (right panel). Diagnostic, or strain-specific SNPs, are singletons for the complete set of genotypes used in the study, including the tester line.

Techniques Used: Genotyping Assay, Diagnostic Assay, Sequencing

10) Product Images from "Deep mutational scans for ACE2 binding, RBD expression, and antibody escape in the SARS-CoV-2 Omicron BA.1 and BA.2 receptor-binding domains"

Article Title: Deep mutational scans for ACE2 binding, RBD expression, and antibody escape in the SARS-CoV-2 Omicron BA.1 and BA.2 receptor-binding domains

Journal: bioRxiv

doi: 10.1101/2022.09.20.508745

Mutant library generation and statistics. (A) Scheme for generation of the Omicron BA.1 and BA.2 RBD mutant libraries. Site saturation mutagenesis oligonucleotide libraries were constructed by Twist Bioscience with constant flank sequences. For each Omicron background, a three-fragment Gibson Assembly was performed with: (1) the pooled mutant RBD oligonucleotide, (2) a PCR-generated oligonucleotide encoding a randomized N16 nucleotide barcode, and (3) linearized vector backbone. PacBio sequencing of the barcoded mutant library plasmid was used to link N16 barcode to mutant RBD sequence, enabling complete definition of library statistics and the creation of a barcode-variant lookup table such that subsequent deep mutational scans only require N16 barcode sequencing. (B-E) For pooled duplicate Omicron BA.1 (top) and BA.2 (bottom) libraries, (B) the average number of mutations of each class per barcoded variant, (C) the distribution of the number of amino acid mutations per barcoded variant, (D) the mutation rate at each site along the RBD sequence, and (E) the distribution of the total number of associated N16 barcodes for each possible amino acid mutation (from filtered ACE2 binding scores).
Figure Legend Snippet: Mutant library generation and statistics. (A) Scheme for generation of the Omicron BA.1 and BA.2 RBD mutant libraries. Site saturation mutagenesis oligonucleotide libraries were constructed by Twist Bioscience with constant flank sequences. For each Omicron background, a three-fragment Gibson Assembly was performed with: (1) the pooled mutant RBD oligonucleotide, (2) a PCR-generated oligonucleotide encoding a randomized N16 nucleotide barcode, and (3) linearized vector backbone. PacBio sequencing of the barcoded mutant library plasmid was used to link N16 barcode to mutant RBD sequence, enabling complete definition of library statistics and the creation of a barcode-variant lookup table such that subsequent deep mutational scans only require N16 barcode sequencing. (B-E) For pooled duplicate Omicron BA.1 (top) and BA.2 (bottom) libraries, (B) the average number of mutations of each class per barcoded variant, (C) the distribution of the number of amino acid mutations per barcoded variant, (D) the mutation rate at each site along the RBD sequence, and (E) the distribution of the total number of associated N16 barcodes for each possible amino acid mutation (from filtered ACE2 binding scores).

Techniques Used: Mutagenesis, Construct, Polymerase Chain Reaction, Generated, Plasmid Preparation, Sequencing, Variant Assay, Binding Assay

11) Product Images from "A large accessory protein interactome is rewired across environments"

Article Title: A large accessory protein interactome is rewired across environments

Journal: bioRxiv

doi: 10.1101/2020.05.20.106583

Double barcodes and protein pairs in the PPiSeq library. (A) Distribution of the initial double barcode count of the PPiSeq library in SD environment at a sequencing depth of 209,899,687 reads. (B) Number of barcodes per protein pair in the PPiSeq library. Spike-in control protein pairs are not included in the plot.
Figure Legend Snippet: Double barcodes and protein pairs in the PPiSeq library. (A) Distribution of the initial double barcode count of the PPiSeq library in SD environment at a sequencing depth of 209,899,687 reads. (B) Number of barcodes per protein pair in the PPiSeq library. Spike-in control protein pairs are not included in the plot.

Techniques Used: Sequencing

PPiSeq (A) A cartoon of PPiSeq yeast library construction. Strains from the protein interactome collection are individually mated to strains from the double barcoder collection and sporulated to recover haploids that contain a mDHFR-tagged protein and a barcode. Haploids are mated as pools. In diploids, expression of Cre recombinase causes recombination between homologous chromosomes at the loxP locus, resulting in a contiguous double barcode that marks the mDHFR-tagged protein pair. (B) Representative double barcode frequency trajectories over twelve generations of competitive growth. Trajectories are used to calculate a quantitative fitness for each double barcoded strain. (C) Standard error of fitness estimates of protein pairs. The blue and red lines represent the median standard error for a sliding window (width = 0.05) of all fitness-ranked protein pairs and of only the positive protein-protein interactions, respectively. (D) Estimated fitness of strains with different double barcodes representing the same protein pair in the same pooled growth. Positive protein pairs are randomly selected within a fitness window. ORF x Null is a violin plot of the fitness distribution of all interactions with a mDHFR fragment that is not tethered to a yeast protein. DHFR(-) is yeast strains that lack any mDHFR fragment. DHFR(+) is yeast strains that contain a full length mDHFR under a strong promoter. (E) Density plot of the fitness of double barcodes that represent the same putative PPI in the same pooled growth. In B-E , the data in SD environment are used. (F) Density plot of the normalized mean fitness of the same PPI between two pooled growth cultures in SD environment. PPIs detected in either one growth culture are included. (G) Venn diagram of the number of PPIs identified within our search space by PPiSeq in 9 environments (magenta), PPiSeq in SD environment (pink), the interactome-scale protein-fragment complementation screen (PCA, yellow), and the BioGRID database excluding any PPIs previously detected by PCA (blue).
Figure Legend Snippet: PPiSeq (A) A cartoon of PPiSeq yeast library construction. Strains from the protein interactome collection are individually mated to strains from the double barcoder collection and sporulated to recover haploids that contain a mDHFR-tagged protein and a barcode. Haploids are mated as pools. In diploids, expression of Cre recombinase causes recombination between homologous chromosomes at the loxP locus, resulting in a contiguous double barcode that marks the mDHFR-tagged protein pair. (B) Representative double barcode frequency trajectories over twelve generations of competitive growth. Trajectories are used to calculate a quantitative fitness for each double barcoded strain. (C) Standard error of fitness estimates of protein pairs. The blue and red lines represent the median standard error for a sliding window (width = 0.05) of all fitness-ranked protein pairs and of only the positive protein-protein interactions, respectively. (D) Estimated fitness of strains with different double barcodes representing the same protein pair in the same pooled growth. Positive protein pairs are randomly selected within a fitness window. ORF x Null is a violin plot of the fitness distribution of all interactions with a mDHFR fragment that is not tethered to a yeast protein. DHFR(-) is yeast strains that lack any mDHFR fragment. DHFR(+) is yeast strains that contain a full length mDHFR under a strong promoter. (E) Density plot of the fitness of double barcodes that represent the same putative PPI in the same pooled growth. In B-E , the data in SD environment are used. (F) Density plot of the normalized mean fitness of the same PPI between two pooled growth cultures in SD environment. PPIs detected in either one growth culture are included. (G) Venn diagram of the number of PPIs identified within our search space by PPiSeq in 9 environments (magenta), PPiSeq in SD environment (pink), the interactome-scale protein-fragment complementation screen (PCA, yellow), and the BioGRID database excluding any PPIs previously detected by PCA (blue).

Techniques Used: Expressing

12) Product Images from "A Multifunctional Anchor for Multimodal Expansion Microscopy"

Article Title: A Multifunctional Anchor for Multimodal Expansion Microscopy

Journal: bioRxiv

doi: 10.1101/2022.06.19.496699

Demonstration of uniExM for in situ RNA sequencing (ExSeq). (A) Amplicons generated by GMA-based uExSeq in HeLa cells were imaged with SBS reagents (from the Illumina MiSeq v3 kit). The following excitation and emission wavelengths were used for 5-channel acquisition: DAPI – Ex. 405 nm / Em. 440-460 nm; Base “G” – Ex. 488 nm / Em. 500-550 nm; Base “T” – Ex. 561 nm / Em. 575-590 nm; Base “A” – Ex. 640 nm / Em. 663-737 nm; Base “C” – Ex. 685 nm / Em. 705-845 nm. Scale bars (in pre-expansion units): 20 µm. (B) Characterization of uniExM for in situ enzymatic amplification in tExSeq. GAPDH mRNAs were amplified by HCR- FISH (ExFISH) or padlock probes in tExSeq. Then the generated signal spots in individual cells were counted. For better comparison, Alexa546 conjugated oligonucleotide probes were used for amplicon detection in both cases. Scale bars (in pre-expansion units): 20 µm. (C) Characterization of uniExM for in situ enzymatic sequencing in tExSeq. tExSeq targeting ACTB mRNAs in Thy1-YFP mouse brain tissues was performed, where padlock probes bearing consecutive bases “TTT” as the barcode were used. Before in situ sequencing, imaging with universal amplicon detection probes was performed to establish a reference image for the transcript locations (lower left, red). YFP signals were also imaged. After that, the universal probes and YFP signals were removed by concentrated formamide and heat treatment. Next, three rounds of SBS were conducted and the detected signal spots were benchmarked against the reference amplicon image (lower row, yellow dots). Scale bars (in pre-expansion units): 20 µm (for upper panel), 5 µm (for lower panel). (D) tExSeq targeting 87 cancer clone-specific genes in SA501 PDX breast cancer tissue was performed using 7-round SBS. All the decoded transcripts from a ∼0.8 mm 2 tissue slice, along with their function annotations, are summarized in the bar chart. (E) Principal component analysis (PCA) identified two groups of genes (15 each) that classify the tissue cells into two primary groups.
Figure Legend Snippet: Demonstration of uniExM for in situ RNA sequencing (ExSeq). (A) Amplicons generated by GMA-based uExSeq in HeLa cells were imaged with SBS reagents (from the Illumina MiSeq v3 kit). The following excitation and emission wavelengths were used for 5-channel acquisition: DAPI – Ex. 405 nm / Em. 440-460 nm; Base “G” – Ex. 488 nm / Em. 500-550 nm; Base “T” – Ex. 561 nm / Em. 575-590 nm; Base “A” – Ex. 640 nm / Em. 663-737 nm; Base “C” – Ex. 685 nm / Em. 705-845 nm. Scale bars (in pre-expansion units): 20 µm. (B) Characterization of uniExM for in situ enzymatic amplification in tExSeq. GAPDH mRNAs were amplified by HCR- FISH (ExFISH) or padlock probes in tExSeq. Then the generated signal spots in individual cells were counted. For better comparison, Alexa546 conjugated oligonucleotide probes were used for amplicon detection in both cases. Scale bars (in pre-expansion units): 20 µm. (C) Characterization of uniExM for in situ enzymatic sequencing in tExSeq. tExSeq targeting ACTB mRNAs in Thy1-YFP mouse brain tissues was performed, where padlock probes bearing consecutive bases “TTT” as the barcode were used. Before in situ sequencing, imaging with universal amplicon detection probes was performed to establish a reference image for the transcript locations (lower left, red). YFP signals were also imaged. After that, the universal probes and YFP signals were removed by concentrated formamide and heat treatment. Next, three rounds of SBS were conducted and the detected signal spots were benchmarked against the reference amplicon image (lower row, yellow dots). Scale bars (in pre-expansion units): 20 µm (for upper panel), 5 µm (for lower panel). (D) tExSeq targeting 87 cancer clone-specific genes in SA501 PDX breast cancer tissue was performed using 7-round SBS. All the decoded transcripts from a ∼0.8 mm 2 tissue slice, along with their function annotations, are summarized in the bar chart. (E) Principal component analysis (PCA) identified two groups of genes (15 each) that classify the tissue cells into two primary groups.

Techniques Used: In Situ, RNA Sequencing Assay, Generated, Amplification, Fluorescence In Situ Hybridization, Sequencing, Imaging

uniExM-supported in situ RNA sequencing (ExSeq). (A) Schematic of the workflow for targeted ExSeq (tExSeq): target RNA molecules are reacted with GMA to acquire methacrylate groups (termed MA-RNA) and anchored to the expandable hydrogel. Padlock probes are then introduced to hybridize with the target RNAs in the expanded biological sample. Upon successful hybridization, the sequence of a target mRNA serves as a “splint” for PBCV-1 enzyme-mediated ligation of the bound padlock, as shown in the zoomed-in panel (i) . Afterwards, rolling circle amplification (RCA) is applied to amplify the ligated probes that harbor barcodes specific to targets. Finally, the barcodes are read out by in situ sequencing chemistry, as shown in the zoomed-in panel (ii) . The full readout of a specific barcode can then be used to reveal the gene identity ( e.g., from barcode_a to gene_a) together with its location information. In such way, multiple gene targets can be decoded (represented as differentially colored amplicons). (B) Validation of ExSeq enzymatics and sequencing-by-synthesis (SBS) chemistry in samples processed with the uniExM procedure. (i) GAPDH in HeLa cells was chosen to undergo HCR-FISH or tExSeq. The numbers of detected signal spots per cell were quantitatively compared. No statistically significant difference was observed between the two methods. (Data shown as violin plots, with raw data points presented, and mean values highlighted with solid lines; n = 70 cells from 4 samples, 2 culture batches; two-sample t- test was performed with p > 0.1) (ii) A fluorescence image showing raw ExSeq signals from all four base channels in HeLa cells undergoing targeted ExSeq. Scale bars (in pre-expansion units): 20 µm. (C) Demonstration of ExSeq applying an 87-gene panel in GMA-anchored SA501 PDX breast cancer tissue. (i) Overview of the raw ExSeq reads (gray spots) in the tissue. DAPI staining for nuclei was used for cell segmentation and reads assignment (shown in the zoomed-in images). (ii) The raw ExSeq reads were decoded and colored based on 8 distinct gene function groups (full list in Supplementary Table 5 ). Scale bars (in pre-expansion units): 100 µm (for stitched overview images), 10 µm (for zoomed-in images). (iii) Gene maps of 3 selected function groups – DNA repair, proliferation and epithelial- mesenchymal transition (EMT), were used to help visualize heterogeneity of cell status in the whole tissue. The decoded transcripts of genes belonging to each functional group were summed and their ratio to total transcript counts were assigned to the “R”, “G”, “B” color channels of the image, respectively. During the color assigning process, a scaling factor of 3.33 (for DNA repair and proliferation) or 2.5 (for EMT) was introduced; that is, if the EMT group of genes was 40% of the total transcripts in one cell, its assigned “B” channel was given the maximum color intensity (40% X 2.5 = 100%). Then, the three individual channels were combined to make a composite image (right). Scale bars (in pre-expansion units): 100 µm. Linear expansion factor: 3.2 (the expanded gel was re-embedded before sequencing). (D) Unsupervised principal component analysis (PCA) identified two primary gene groups for cell classification. (i) Using these two PCA gene groups, a distribution of different cancer cells was revealed. In this presented image, the summed transcripts of each PCA group normalized to the total transcript count for a given cell were assigned to the “R” channel and “G” channel, respectively (scaling factor: 3.33). Then the two images were overlaid to make a composite. Scale bar (in pre-expansion units): 100 µm. (ii) In the zoomed-in region where differentially colored cells co-exist, 6 marker genes are plotted; their distribution varies across cells in the subregion. Scale bar (in pre-expansion units): 10 µm. (E) Uniform manifold approximation and projection (UMAP) representation of the cell typing results using bulk RNA-seq identified marker genes in the SA501 cancer model. According to the panel design, the 87 gene list could differentiate two primary cancer cell clones that are successfully annotated on UMAP – “Tumor_XIST” and “Tumor_ZNF24”, named after their feature genes. A small group of cells are marked as “Unclassified”, likely attributed to non-cancer interstitial or other cells.
Figure Legend Snippet: uniExM-supported in situ RNA sequencing (ExSeq). (A) Schematic of the workflow for targeted ExSeq (tExSeq): target RNA molecules are reacted with GMA to acquire methacrylate groups (termed MA-RNA) and anchored to the expandable hydrogel. Padlock probes are then introduced to hybridize with the target RNAs in the expanded biological sample. Upon successful hybridization, the sequence of a target mRNA serves as a “splint” for PBCV-1 enzyme-mediated ligation of the bound padlock, as shown in the zoomed-in panel (i) . Afterwards, rolling circle amplification (RCA) is applied to amplify the ligated probes that harbor barcodes specific to targets. Finally, the barcodes are read out by in situ sequencing chemistry, as shown in the zoomed-in panel (ii) . The full readout of a specific barcode can then be used to reveal the gene identity ( e.g., from barcode_a to gene_a) together with its location information. In such way, multiple gene targets can be decoded (represented as differentially colored amplicons). (B) Validation of ExSeq enzymatics and sequencing-by-synthesis (SBS) chemistry in samples processed with the uniExM procedure. (i) GAPDH in HeLa cells was chosen to undergo HCR-FISH or tExSeq. The numbers of detected signal spots per cell were quantitatively compared. No statistically significant difference was observed between the two methods. (Data shown as violin plots, with raw data points presented, and mean values highlighted with solid lines; n = 70 cells from 4 samples, 2 culture batches; two-sample t- test was performed with p > 0.1) (ii) A fluorescence image showing raw ExSeq signals from all four base channels in HeLa cells undergoing targeted ExSeq. Scale bars (in pre-expansion units): 20 µm. (C) Demonstration of ExSeq applying an 87-gene panel in GMA-anchored SA501 PDX breast cancer tissue. (i) Overview of the raw ExSeq reads (gray spots) in the tissue. DAPI staining for nuclei was used for cell segmentation and reads assignment (shown in the zoomed-in images). (ii) The raw ExSeq reads were decoded and colored based on 8 distinct gene function groups (full list in Supplementary Table 5 ). Scale bars (in pre-expansion units): 100 µm (for stitched overview images), 10 µm (for zoomed-in images). (iii) Gene maps of 3 selected function groups – DNA repair, proliferation and epithelial- mesenchymal transition (EMT), were used to help visualize heterogeneity of cell status in the whole tissue. The decoded transcripts of genes belonging to each functional group were summed and their ratio to total transcript counts were assigned to the “R”, “G”, “B” color channels of the image, respectively. During the color assigning process, a scaling factor of 3.33 (for DNA repair and proliferation) or 2.5 (for EMT) was introduced; that is, if the EMT group of genes was 40% of the total transcripts in one cell, its assigned “B” channel was given the maximum color intensity (40% X 2.5 = 100%). Then, the three individual channels were combined to make a composite image (right). Scale bars (in pre-expansion units): 100 µm. Linear expansion factor: 3.2 (the expanded gel was re-embedded before sequencing). (D) Unsupervised principal component analysis (PCA) identified two primary gene groups for cell classification. (i) Using these two PCA gene groups, a distribution of different cancer cells was revealed. In this presented image, the summed transcripts of each PCA group normalized to the total transcript count for a given cell were assigned to the “R” channel and “G” channel, respectively (scaling factor: 3.33). Then the two images were overlaid to make a composite. Scale bar (in pre-expansion units): 100 µm. (ii) In the zoomed-in region where differentially colored cells co-exist, 6 marker genes are plotted; their distribution varies across cells in the subregion. Scale bar (in pre-expansion units): 10 µm. (E) Uniform manifold approximation and projection (UMAP) representation of the cell typing results using bulk RNA-seq identified marker genes in the SA501 cancer model. According to the panel design, the 87 gene list could differentiate two primary cancer cell clones that are successfully annotated on UMAP – “Tumor_XIST” and “Tumor_ZNF24”, named after their feature genes. A small group of cells are marked as “Unclassified”, likely attributed to non-cancer interstitial or other cells.

Techniques Used: In Situ, RNA Sequencing Assay, Hybridization, Sequencing, Ligation, Amplification, Fluorescence In Situ Hybridization, Fluorescence, Staining, Functional Assay, Marker, Clone Assay

13) Product Images from "Meiotic, genomic and evolutionary properties of crossover distribution in Drosophila yakuba"

Article Title: Meiotic, genomic and evolutionary properties of crossover distribution in Drosophila yakuba

Journal: bioRxiv

doi: 10.1101/2022.02.10.479767

Dual-barcoding genotyping method used to obtain crossover rates. Diagnostic SNPs are used as genetic barcodes and allow for the pooling of several F 2 individuals from different crosses for a given sequence barcode (left panel). The combination of genetic and sequence barcodes ensures efficient genotyping of multiple individuals and accurate crossover localization along chromosome arms (right panel). Diagnostic, or strain-specific SNPs, are singletons for the complete set of genotypes used in the study, including the tester line.
Figure Legend Snippet: Dual-barcoding genotyping method used to obtain crossover rates. Diagnostic SNPs are used as genetic barcodes and allow for the pooling of several F 2 individuals from different crosses for a given sequence barcode (left panel). The combination of genetic and sequence barcodes ensures efficient genotyping of multiple individuals and accurate crossover localization along chromosome arms (right panel). Diagnostic, or strain-specific SNPs, are singletons for the complete set of genotypes used in the study, including the tester line.

Techniques Used: Genotyping Assay, Diagnostic Assay, Sequencing

14) Product Images from "Highly accurate barcode and UMI error correction using dual nucleotide dimer blocks allows direct single-cell nanopore transcriptome sequencing"

Article Title: Highly accurate barcode and UMI error correction using dual nucleotide dimer blocks allows direct single-cell nanopore transcriptome sequencing

Journal: bioRxiv

doi: 10.1101/2021.01.18.427145

Developing a strategy to error correct barcode and UMI sequences from droplet-based sequencing. a Schematic bead and oligonucleotide structure using dimer blocks of nucleotides for BUC-seq. b Cell barcode assignment strategy. c UMI deduplication strategy. d Simulated data showing the number of barcodes recovered with increasing simulated sequencing error rates. e, f Simulated data showing the difference and coefficient of variation between the deduplicated UMIs and the ground truth. Deduplication was performed using a basic directional network-based approach and accounting for sequencing errors within paired nucleotides.
Figure Legend Snippet: Developing a strategy to error correct barcode and UMI sequences from droplet-based sequencing. a Schematic bead and oligonucleotide structure using dimer blocks of nucleotides for BUC-seq. b Cell barcode assignment strategy. c UMI deduplication strategy. d Simulated data showing the number of barcodes recovered with increasing simulated sequencing error rates. e, f Simulated data showing the difference and coefficient of variation between the deduplicated UMIs and the ground truth. Deduplication was performed using a basic directional network-based approach and accounting for sequencing errors within paired nucleotides.

Techniques Used: Sequencing

Error correction of both Illumina and Nanopore droplet based scRNA-seq data Human HEK293T and mouse 3T3 were mixed at a 1:1 ratio and approximately 500 cells were taken for encapsulation and cDNA synthesis. Barcodes and UMIs identified as having at least one sequencing error were processed a before and b after barcode error correction. The proportion of mouse and human UMIs are shown in the Barnyard plot. Insert bar plots show the number of cells identified for each species. c The length of the input cDNA Nanopore library, as measured using a tapestation. d The read length of the sequenced Nanopore library. e The percent of reads that have a polyA tail. The percent of polyA + reads that show perfect based on the nucleotide pairing complementarity and the percent of reads that ccan be recovered using an Levenshtein distance of 6. Boxes and error bars indicate the means and standard deviations for n=4 individual experiments. Barnyard plots showing the expression of mouse and human UMIs using a g Levenshtein distance (LD) of 6 and a h Levenshtein distance of 7. The insert bar plots show the number of cells recovered for each species. UMAP plots of the showing human, mouse or mixed human and mouse cells when barcodes are corrected using a i Levenshtein distance of 6 or a j Levenshtein distance of 7.
Figure Legend Snippet: Error correction of both Illumina and Nanopore droplet based scRNA-seq data Human HEK293T and mouse 3T3 were mixed at a 1:1 ratio and approximately 500 cells were taken for encapsulation and cDNA synthesis. Barcodes and UMIs identified as having at least one sequencing error were processed a before and b after barcode error correction. The proportion of mouse and human UMIs are shown in the Barnyard plot. Insert bar plots show the number of cells identified for each species. c The length of the input cDNA Nanopore library, as measured using a tapestation. d The read length of the sequenced Nanopore library. e The percent of reads that have a polyA tail. The percent of polyA + reads that show perfect based on the nucleotide pairing complementarity and the percent of reads that ccan be recovered using an Levenshtein distance of 6. Boxes and error bars indicate the means and standard deviations for n=4 individual experiments. Barnyard plots showing the expression of mouse and human UMIs using a g Levenshtein distance (LD) of 6 and a h Levenshtein distance of 7. The insert bar plots show the number of cells recovered for each species. UMAP plots of the showing human, mouse or mixed human and mouse cells when barcodes are corrected using a i Levenshtein distance of 6 or a j Levenshtein distance of 7.

Techniques Used: Sequencing, Expressing

15) Product Images from "A systematic comparison reveals substantial differences in chromosomal versus episomal encoding of enhancer activity"

Article Title: A systematic comparison reveals substantial differences in chromosomal versus episomal encoding of enhancer activity

Journal: Genome Research

doi: 10.1101/gr.212092.116

Study design for lentiMPRA. ( A ) Schematic diagram of lentiMPRA. Candidate enhancers and barcode tags were synthesized in tandem as a microarray-derived oligonucleotide library and cloned into the pLS-mP vector, followed by cloning of a minimal promoter
Figure Legend Snippet: Study design for lentiMPRA. ( A ) Schematic diagram of lentiMPRA. Candidate enhancers and barcode tags were synthesized in tandem as a microarray-derived oligonucleotide library and cloned into the pLS-mP vector, followed by cloning of a minimal promoter

Techniques Used: Synthesized, Microarray, Derivative Assay, Clone Assay, Plasmid Preparation

16) Product Images from "High-throughput Tetrad Analysis"

Article Title: High-throughput Tetrad Analysis

Journal: Nature methods

doi: 10.1038/nmeth.2479

BEST method ( a ) A pool of barcoded plasmids is transformed into the diploid strain from a cross. ( b ) Transformants are then grown on selection and ~10,000 transformed colonies are pooled and sporulated. During meiosis each spore of a tetrad inherits a copy of the barcoded plasmid. ( c ) Tetrads are separated from unsporulated cells by FACS and collected on agar plates, where they are digested and disrupted to allow each spore to form a colony. ( e ) Colonies are then picked into 96 well plates, phenotyped and genotyped. During genotyping the plasmid barcode is read and used to identify the four members of each tetrad.
Figure Legend Snippet: BEST method ( a ) A pool of barcoded plasmids is transformed into the diploid strain from a cross. ( b ) Transformants are then grown on selection and ~10,000 transformed colonies are pooled and sporulated. During meiosis each spore of a tetrad inherits a copy of the barcoded plasmid. ( c ) Tetrads are separated from unsporulated cells by FACS and collected on agar plates, where they are digested and disrupted to allow each spore to form a colony. ( e ) Colonies are then picked into 96 well plates, phenotyped and genotyped. During genotyping the plasmid barcode is read and used to identify the four members of each tetrad.

Techniques Used: Transformation Assay, Selection, Plasmid Preparation, FACS

Sequence-based tetrad reconstruction and genotyping The progeny strains from a cross are genotyped by sequencing, and during this process the plasmid barcode of each strain is read. ( a ) The members of a single tetrad can be established by identifying four strains that share a common plasmid barcode. ( b ) Once strains have been assigned to tetrads, missing markers can be inferred and a full genome sequence with recombination events can be deduced for each strain.
Figure Legend Snippet: Sequence-based tetrad reconstruction and genotyping The progeny strains from a cross are genotyped by sequencing, and during this process the plasmid barcode of each strain is read. ( a ) The members of a single tetrad can be established by identifying four strains that share a common plasmid barcode. ( b ) Once strains have been assigned to tetrads, missing markers can be inferred and a full genome sequence with recombination events can be deduced for each strain.

Techniques Used: Sequencing, Plasmid Preparation

17) Product Images from "Quantitative phenotyping via deep barcode sequencing"

Article Title: Quantitative phenotyping via deep barcode sequencing

Journal: Genome Research

doi: 10.1101/gr.093955.109

Results of the yeast deletion pools assayed by array and Bar-seq. Log 2 results for both TAG4 barcode microarray hybridization and Illumina sequencing are presented. All axes represent log 2 ratios of control over treatment vs. genes (alphabetically ordered).
Figure Legend Snippet: Results of the yeast deletion pools assayed by array and Bar-seq. Log 2 results for both TAG4 barcode microarray hybridization and Illumina sequencing are presented. All axes represent log 2 ratios of control over treatment vs. genes (alphabetically ordered).

Techniques Used: Microarray, Hybridization, Sequencing

Comparison of barcode microarray hybridization and Bar-seq data on identical samples. A pool of 953 strains was created that contains four subpools of approximately 250 yeast deletion strains each. The strains in this pool were selected to contain two
Figure Legend Snippet: Comparison of barcode microarray hybridization and Bar-seq data on identical samples. A pool of 953 strains was created that contains four subpools of approximately 250 yeast deletion strains each. The strains in this pool were selected to contain two

Techniques Used: Microarray, Hybridization

Schematic showing sequencing strategy for re-characterization of barcode and common priming sequences. (U1, U2/D1, D2) Common priming sites for uptag/downtag barcodes. (BC) Barcode. ( Top panels) We used a paired-end sequencing reaction to identify both
Figure Legend Snippet: Schematic showing sequencing strategy for re-characterization of barcode and common priming sequences. (U1, U2/D1, D2) Common priming sites for uptag/downtag barcodes. (BC) Barcode. ( Top panels) We used a paired-end sequencing reaction to identify both

Techniques Used: Sequencing

18) Product Images from "Generalized DNA Barcode Design Based on Hamming Codes"

Article Title: Generalized DNA Barcode Design Based on Hamming Codes

Journal: PLoS ONE

doi: 10.1371/journal.pone.0036852

A concept of Hamming error correction in quaternary format. A 7-base sequence is indexed by position and value of each base is provided. With those values checksums are calculated and possible error is detected (in the given example “T” is an error). Max(Ch i ) = 2 gives the type of the error, sequence Ch 3 ,Ch 2 ,Ch 1 = 202 is transformed to binary 101 (with the rule: if Ch i > 0 then Ch i = 1 ), which is equal to decimal 5. This defines position of the error. Since the value at erroneous position is 3 (for C s = ”T” S = 3), the correct value should be 3−2 = 1. For S = 1, C s = ”C”. Thus, the barcode should be corrected at the position 5, the correct base is “C”. Note when calculating correct base: if S true
Figure Legend Snippet: A concept of Hamming error correction in quaternary format. A 7-base sequence is indexed by position and value of each base is provided. With those values checksums are calculated and possible error is detected (in the given example “T” is an error). Max(Ch i ) = 2 gives the type of the error, sequence Ch 3 ,Ch 2 ,Ch 1 = 202 is transformed to binary 101 (with the rule: if Ch i > 0 then Ch i = 1 ), which is equal to decimal 5. This defines position of the error. Since the value at erroneous position is 3 (for C s = ”T” S = 3), the correct value should be 3−2 = 1. For S = 1, C s = ”C”. Thus, the barcode should be corrected at the position 5, the correct base is “C”. Note when calculating correct base: if S true

Techniques Used: Sequencing, Transformation Assay

19) Product Images from "Systematic identification of cis-regulatory variants that cause gene expression differences in a yeast cross"

Article Title: Systematic identification of cis-regulatory variants that cause gene expression differences in a yeast cross

Journal: eLife

doi: 10.7554/eLife.62669

Barcode amplification. The figure shows details of the molecular reactions used to make Illumina sequencing libraries for barcode counting. Primer names are given in quotes. For RNA, Protocol one is on the left and Protocol two is on the right (see Materials and methods for details). Note that in Protocol 1, the PCR step can exponentially amplify only cDNA but not plasmid molecules that may have escaped DNA degradation during RNA extraction because both PCR primers bind to overhangs added in the previous steps. If, during the single extension step, ‘common_ORF_v4’ uses plasmid DNA as a template, the product lacks the p5 overhang, which contains the binding site for ‘Illumina_PCR_R’. Conversely, if during single extension, ‘RT_PCR_R_long’ (which is still present in the reaction) primes off a plasmid molecule, the product lacks the p7 overhang required by ‘RT_PCR_D7xx_F’. In protocol 2, the primers ‘common_ORF_v2’ and ‘RT_PCRPD7xx_F’ are replaced by primers ‘RT_PCR_F_long_D7xx’. These primers permit direct amplification from plasmids and from first strand cDNA but require multiple long primers for multiplexing and provide less protection against inadvertent plasmid amplification.
Figure Legend Snippet: Barcode amplification. The figure shows details of the molecular reactions used to make Illumina sequencing libraries for barcode counting. Primer names are given in quotes. For RNA, Protocol one is on the left and Protocol two is on the right (see Materials and methods for details). Note that in Protocol 1, the PCR step can exponentially amplify only cDNA but not plasmid molecules that may have escaped DNA degradation during RNA extraction because both PCR primers bind to overhangs added in the previous steps. If, during the single extension step, ‘common_ORF_v4’ uses plasmid DNA as a template, the product lacks the p5 overhang, which contains the binding site for ‘Illumina_PCR_R’. Conversely, if during single extension, ‘RT_PCR_R_long’ (which is still present in the reaction) primes off a plasmid molecule, the product lacks the p7 overhang required by ‘RT_PCR_D7xx_F’. In protocol 2, the primers ‘common_ORF_v2’ and ‘RT_PCRPD7xx_F’ are replaced by primers ‘RT_PCR_F_long_D7xx’. These primers permit direct amplification from plasmids and from first strand cDNA but require multiple long primers for multiplexing and provide less protection against inadvertent plasmid amplification.

Techniques Used: Amplification, Sequencing, Polymerase Chain Reaction, Plasmid Preparation, RNA Extraction, Binding Assay, Reverse Transcription Polymerase Chain Reaction, Multiplexing

Distributions of barcodes. ( A ) Number of barcodes tagging a given oligo in the TSS library. The inset shows the range from zero to 5000 barcodes, which contains the majority of the distribution. ( B ) as in ( A ), but for the Upstream library. ( C ) Distribution of the number of times a given barcode was observed in the TSS annotation sequencing run. ( D ). As in ( C ), but for the Upstream library.
Figure Legend Snippet: Distributions of barcodes. ( A ) Number of barcodes tagging a given oligo in the TSS library. The inset shows the range from zero to 5000 barcodes, which contains the majority of the distribution. ( B ) as in ( A ), but for the Upstream library. ( C ) Distribution of the number of times a given barcode was observed in the TSS annotation sequencing run. ( D ). As in ( C ), but for the Upstream library.

Techniques Used: Sequencing

20) Product Images from "A large accessory protein interactome is rewired across environments"

Article Title: A large accessory protein interactome is rewired across environments

Journal: eLife

doi: 10.7554/eLife.62365

Double barcodes and protein pairs in the PPiSeq library. ( A ) Distribution of the initial double barcode count of the PPiSeq library in SD environment at a sequencing depth of 209,899,687 reads. ( B ) Number of barcodes per protein pair in the PPiSeq library. Spike-in control protein pairs are not included in the plot.
Figure Legend Snippet: Double barcodes and protein pairs in the PPiSeq library. ( A ) Distribution of the initial double barcode count of the PPiSeq library in SD environment at a sequencing depth of 209,899,687 reads. ( B ) Number of barcodes per protein pair in the PPiSeq library. Spike-in control protein pairs are not included in the plot.

Techniques Used: Sequencing

21) Product Images from "A large accessory protein interactome is rewired across environments"

Article Title: A large accessory protein interactome is rewired across environments

Journal: bioRxiv

doi: 10.1101/2020.05.20.106583

Double barcodes and protein pairs in the PPiSeq library. (A) Distribution of the initial double barcode count of the PPiSeq library in SD environment at a sequencing depth of 209,899,687 reads. (B) Number of barcodes per protein pair in the PPiSeq library. Spike-in control protein pairs are not included in the plot.
Figure Legend Snippet: Double barcodes and protein pairs in the PPiSeq library. (A) Distribution of the initial double barcode count of the PPiSeq library in SD environment at a sequencing depth of 209,899,687 reads. (B) Number of barcodes per protein pair in the PPiSeq library. Spike-in control protein pairs are not included in the plot.

Techniques Used: Sequencing

PPiSeq (A) A cartoon of PPiSeq yeast library construction. Strains from the protein interactome collection are individually mated to strains from the double barcoder collection and sporulated to recover haploids that contain a mDHFR-tagged protein and a barcode. Haploids are mated as pools. In diploids, expression of Cre recombinase causes recombination between homologous chromosomes at the loxP locus, resulting in a contiguous double barcode that marks the mDHFR-tagged protein pair. (B) Representative double barcode frequency trajectories over twelve generations of competitive growth. Trajectories are used to calculate a quantitative fitness for each double barcoded strain. (C) Standard error of fitness estimates of protein pairs. The blue and red lines represent the median standard error for a sliding window (width = 0.05) of all fitness-ranked protein pairs and of only the positive protein-protein interactions, respectively. (D) Estimated fitness of strains with different double barcodes representing the same protein pair in the same pooled growth. Positive protein pairs are randomly selected within a fitness window. ORF x Null is a violin plot of the fitness distribution of all interactions with a mDHFR fragment that is not tethered to a yeast protein. DHFR(-) is yeast strains that lack any mDHFR fragment. DHFR(+) is yeast strains that contain a full length mDHFR under a strong promoter. (E) Density plot of the fitness of double barcodes that represent the same putative PPI in the same pooled growth. In B-E , the data in SD environment are used. (F) Density plot of the normalized mean fitness of the same PPI between two pooled growth cultures in SD environment. PPIs detected in either one growth culture are included. (G) Venn diagram of the number of PPIs identified within our search space by PPiSeq in 9 environments (magenta), PPiSeq in SD environment (pink), the interactome-scale protein-fragment complementation screen (PCA, yellow), and the BioGRID database excluding any PPIs previously detected by PCA (blue).
Figure Legend Snippet: PPiSeq (A) A cartoon of PPiSeq yeast library construction. Strains from the protein interactome collection are individually mated to strains from the double barcoder collection and sporulated to recover haploids that contain a mDHFR-tagged protein and a barcode. Haploids are mated as pools. In diploids, expression of Cre recombinase causes recombination between homologous chromosomes at the loxP locus, resulting in a contiguous double barcode that marks the mDHFR-tagged protein pair. (B) Representative double barcode frequency trajectories over twelve generations of competitive growth. Trajectories are used to calculate a quantitative fitness for each double barcoded strain. (C) Standard error of fitness estimates of protein pairs. The blue and red lines represent the median standard error for a sliding window (width = 0.05) of all fitness-ranked protein pairs and of only the positive protein-protein interactions, respectively. (D) Estimated fitness of strains with different double barcodes representing the same protein pair in the same pooled growth. Positive protein pairs are randomly selected within a fitness window. ORF x Null is a violin plot of the fitness distribution of all interactions with a mDHFR fragment that is not tethered to a yeast protein. DHFR(-) is yeast strains that lack any mDHFR fragment. DHFR(+) is yeast strains that contain a full length mDHFR under a strong promoter. (E) Density plot of the fitness of double barcodes that represent the same putative PPI in the same pooled growth. In B-E , the data in SD environment are used. (F) Density plot of the normalized mean fitness of the same PPI between two pooled growth cultures in SD environment. PPIs detected in either one growth culture are included. (G) Venn diagram of the number of PPIs identified within our search space by PPiSeq in 9 environments (magenta), PPiSeq in SD environment (pink), the interactome-scale protein-fragment complementation screen (PCA, yellow), and the BioGRID database excluding any PPIs previously detected by PCA (blue).

Techniques Used: Expressing

22) Product Images from "Quantitative stability of hematopoietic stem and progenitor cell clonal output in rhesus macaques receiving transplants"

Article Title: Quantitative stability of hematopoietic stem and progenitor cell clonal output in rhesus macaques receiving transplants

Journal: Blood

doi: 10.1182/blood-2016-07-728691

Rhesus macaque autologous transplantation and hematopoietic barcoding. (A) Experimental summary. The replication-incompetent HIV-derived lentiviral barcoding vector used is diagrammed at the top right. The barcode consists of a 6–base pair library identification (ID) followed by a 35–base pair high-diversity cellular barcode. This vector was used to transduce rhesus macaque CD34 + cells, and these cells were reinfused after myeloablative total-body irradiation (TBI; 1000 rads) of the autologous recipient. Purified blood cells from various lineages underwent low-cycle polymerase chain reaction (PCR) amplification utilizing primers bracketing the barcode (red arrows in diagram), followed by Illumina sequencing and data processing, as described in supplemental Data. (B) Transplantation and engraftment parameters. The table summarizes CD34 + cell collection, transduction, and transplantation parameters, as well as total clone numbers and clone frequencies for each animal, after applying the threshold of a clone contributing at least 0.05% to at least 1 cell type at a minimum of at least 1 time point. GFP, green fluorescent protein; FACS, fluorescence-activated cell sorting; LTR, long terminal repeat; WPRE, woodchuck hepatitis posttranscriptional regulatory element.
Figure Legend Snippet: Rhesus macaque autologous transplantation and hematopoietic barcoding. (A) Experimental summary. The replication-incompetent HIV-derived lentiviral barcoding vector used is diagrammed at the top right. The barcode consists of a 6–base pair library identification (ID) followed by a 35–base pair high-diversity cellular barcode. This vector was used to transduce rhesus macaque CD34 + cells, and these cells were reinfused after myeloablative total-body irradiation (TBI; 1000 rads) of the autologous recipient. Purified blood cells from various lineages underwent low-cycle polymerase chain reaction (PCR) amplification utilizing primers bracketing the barcode (red arrows in diagram), followed by Illumina sequencing and data processing, as described in supplemental Data. (B) Transplantation and engraftment parameters. The table summarizes CD34 + cell collection, transduction, and transplantation parameters, as well as total clone numbers and clone frequencies for each animal, after applying the threshold of a clone contributing at least 0.05% to at least 1 cell type at a minimum of at least 1 time point. GFP, green fluorescent protein; FACS, fluorescence-activated cell sorting; LTR, long terminal repeat; WPRE, woodchuck hepatitis posttranscriptional regulatory element.

Techniques Used: Transplantation Assay, Derivative Assay, Plasmid Preparation, Transduction, Irradiation, Purification, Polymerase Chain Reaction, Amplification, Sequencing, FACS, Fluorescence

Stability of granulopoiesis over time. (A) Heat map showing the natural log fractional abundances of the highest contributing granulocyte (Gr) barcodes (clones) in ZH33 and ZG66 over time, defined as the set of all barcodes present as a top 100 highest contributing barcode in ≥1 of the samples shown. Each row corresponds to 1 barcode. The barcodes are organized by unsupervised hierarchical clustering using the Euclidean distance between barcodes’ log fractional abundances, with relative contribution shown as a red-to-blue gradient, representing high contribution to no contribution, respectively. Clones are clustered along the y-axis to place similar clones next to each other. *Indicates the top 100 clones in a given sample; thus, there are 100 in each column and ≥1 in each row. (B) The Pearson correlations between Gr clonalities at a certain time point post-transplantation and all subsequent Gr clonalities are plotted as individual lines for ZH33 and ZG66. Each time point is shown as a different color line, so, for example, the correlation of the 1-month sample with the 2-month sample is shown by the position of the red line at the 2-month time point on the y-axis. (C) The total clonal repertoires of barcoded granulopoiesis are displayed for ZH33 and ZG66 as cumulative distribution curves (supplemental Data provide more information on the analytic methodology used). Each position on the x-axis is an individual clone, and each line is the cumulative distribution of clonal contributions at the specified time point. Note that although these clones appear as lines, they are actually discrete sets of points (clones). The height of the line at an index on the x-axis is the sum of the clonal contributions of the clones with an index less than or equal to the index in question. Lower indices indicate earlier clones, and the clone ordering on the x-axis is determined by time of maximum contribution, showing emergence of new clones over time. This ordering is shared among time points, enabling comparison of the behavior of individual clones across time. Because contribution is assessed fractionally, the y-axis is from 0 to 1.
Figure Legend Snippet: Stability of granulopoiesis over time. (A) Heat map showing the natural log fractional abundances of the highest contributing granulocyte (Gr) barcodes (clones) in ZH33 and ZG66 over time, defined as the set of all barcodes present as a top 100 highest contributing barcode in ≥1 of the samples shown. Each row corresponds to 1 barcode. The barcodes are organized by unsupervised hierarchical clustering using the Euclidean distance between barcodes’ log fractional abundances, with relative contribution shown as a red-to-blue gradient, representing high contribution to no contribution, respectively. Clones are clustered along the y-axis to place similar clones next to each other. *Indicates the top 100 clones in a given sample; thus, there are 100 in each column and ≥1 in each row. (B) The Pearson correlations between Gr clonalities at a certain time point post-transplantation and all subsequent Gr clonalities are plotted as individual lines for ZH33 and ZG66. Each time point is shown as a different color line, so, for example, the correlation of the 1-month sample with the 2-month sample is shown by the position of the red line at the 2-month time point on the y-axis. (C) The total clonal repertoires of barcoded granulopoiesis are displayed for ZH33 and ZG66 as cumulative distribution curves (supplemental Data provide more information on the analytic methodology used). Each position on the x-axis is an individual clone, and each line is the cumulative distribution of clonal contributions at the specified time point. Note that although these clones appear as lines, they are actually discrete sets of points (clones). The height of the line at an index on the x-axis is the sum of the clonal contributions of the clones with an index less than or equal to the index in question. Lower indices indicate earlier clones, and the clone ordering on the x-axis is determined by time of maximum contribution, showing emergence of new clones over time. This ordering is shared among time points, enabling comparison of the behavior of individual clones across time. Because contribution is assessed fractionally, the y-axis is from 0 to 1.

Techniques Used: Clone Assay, Transplantation Assay

Stability of lymphopoiesis over time. (A) Heat map showing the natural log fractional abundances of the highest contributing T-cell (T; top) and B-cell (B; bottom) barcodes (clones) in ZH33 over time, defined as the set of all barcodes present as a top 100 highest contributing barcode in ≥1 of the samples shown. Each row corresponds to 1 barcode. The barcodes are organized by unsupervised hierarchical clustering using the Euclidean distance between barcodes’ log fractional abundances, with relative contribution shown as a red-to-blue gradient, representing high contribution to no contribution, respectively. Clones are clustered along the y-axis to place similar clones next to each other. *Indicates the top 100 clones in a given sample; thus, there are 100 in each column and ≥1 in each row. (B) Pearson correlations between T-cell (top) and B-cell (bottom) clonalities in ZH33 at a certain time point posttransplantation and all subsequent T-cell or B-cell clonalities are plotted as individual lines. Each time point is shown as a different color line, so, for example, the correlation of the 1-month sample with the 2-month sample is shown by the position of the red line at the 2-month time point on the y-axis. These correlations depict the stability of T-cell or B-cell clonality over time. (C) The total clonal repertoires of barcoded T-cell (top) and B-cell (bottom) populations are displayed as cumulative distribution curves over time for ZH33. Each position on the x-axis is an individual clone, and each line is the cumulative distribution of clonal contributions at the specified time point. That is, the height of the line at an index on the x-axis is the sum of the clonal contributions of the clones with an index less than or equal to the index in question, at the time point being plotted. Lower indices indicate earlier clones, and the clone ordering on the x-axis is determined by time of maximum contribution. The ordering is shared among time points, enabling comparison of the behavior of individual clones across time. Thus, this figure shows emergence of new clones over time. Because contribution is assessed fractionally, the y-axis is from 0 to 1.
Figure Legend Snippet: Stability of lymphopoiesis over time. (A) Heat map showing the natural log fractional abundances of the highest contributing T-cell (T; top) and B-cell (B; bottom) barcodes (clones) in ZH33 over time, defined as the set of all barcodes present as a top 100 highest contributing barcode in ≥1 of the samples shown. Each row corresponds to 1 barcode. The barcodes are organized by unsupervised hierarchical clustering using the Euclidean distance between barcodes’ log fractional abundances, with relative contribution shown as a red-to-blue gradient, representing high contribution to no contribution, respectively. Clones are clustered along the y-axis to place similar clones next to each other. *Indicates the top 100 clones in a given sample; thus, there are 100 in each column and ≥1 in each row. (B) Pearson correlations between T-cell (top) and B-cell (bottom) clonalities in ZH33 at a certain time point posttransplantation and all subsequent T-cell or B-cell clonalities are plotted as individual lines. Each time point is shown as a different color line, so, for example, the correlation of the 1-month sample with the 2-month sample is shown by the position of the red line at the 2-month time point on the y-axis. These correlations depict the stability of T-cell or B-cell clonality over time. (C) The total clonal repertoires of barcoded T-cell (top) and B-cell (bottom) populations are displayed as cumulative distribution curves over time for ZH33. Each position on the x-axis is an individual clone, and each line is the cumulative distribution of clonal contributions at the specified time point. That is, the height of the line at an index on the x-axis is the sum of the clonal contributions of the clones with an index less than or equal to the index in question, at the time point being plotted. Lower indices indicate earlier clones, and the clone ordering on the x-axis is determined by time of maximum contribution. The ordering is shared among time points, enabling comparison of the behavior of individual clones across time. Thus, this figure shows emergence of new clones over time. Because contribution is assessed fractionally, the y-axis is from 0 to 1.

Techniques Used: Clone Assay

Similarities between clonal contributions to hematopoietic cell types. (A) Pearson correlation measures similarity between the sets of clones from which specific cell populations are descended. Each line gives the Pearson correlations over time between samples from 2 cell types, with each line consisting of 2 colors corresponding to the cell types being compared (T cells [T], black; B cells [B], magenta; monocytes [Mono], green; granulocytes [Gr], light blue). Mono-Gr clonalities are most similar, followed by Gr-B or Mono-B and Gr/B-T or Mono/B-T. These similarities generally stabilize, with greatest volatility in correlation comparisons involving T cells. (B) Heat map showing the natural log fractional abundances of the highest contributing clones in ZH33 over time, defined as the set of all barcodes present as a top 10 highest contributing barcode in ≥1 of the samples shown. Each row corresponds to 1 barcode. The barcodes are organized by unsupervised hierarchical clustering using the Euclidean distance between barcodes’ log fractional abundances, with relative contribution shown as a magenta-to-light blue gradient, representing high contribution to no contribution, respectively. Clones are clustered along the y-axis to place similar clones next to each other. *Indicates the top 10 clones in a given sample; thus, there are 10 in each column and ≥1 in each row. Note transient clones contribute to each lineage at 1 month and then disappear, replaced by multipotent clones beginning at months 2 to 3, with contributions persisting long term.
Figure Legend Snippet: Similarities between clonal contributions to hematopoietic cell types. (A) Pearson correlation measures similarity between the sets of clones from which specific cell populations are descended. Each line gives the Pearson correlations over time between samples from 2 cell types, with each line consisting of 2 colors corresponding to the cell types being compared (T cells [T], black; B cells [B], magenta; monocytes [Mono], green; granulocytes [Gr], light blue). Mono-Gr clonalities are most similar, followed by Gr-B or Mono-B and Gr/B-T or Mono/B-T. These similarities generally stabilize, with greatest volatility in correlation comparisons involving T cells. (B) Heat map showing the natural log fractional abundances of the highest contributing clones in ZH33 over time, defined as the set of all barcodes present as a top 10 highest contributing barcode in ≥1 of the samples shown. Each row corresponds to 1 barcode. The barcodes are organized by unsupervised hierarchical clustering using the Euclidean distance between barcodes’ log fractional abundances, with relative contribution shown as a magenta-to-light blue gradient, representing high contribution to no contribution, respectively. Clones are clustered along the y-axis to place similar clones next to each other. *Indicates the top 10 clones in a given sample; thus, there are 10 in each column and ≥1 in each row. Note transient clones contribute to each lineage at 1 month and then disappear, replaced by multipotent clones beginning at months 2 to 3, with contributions persisting long term.

Techniques Used: Clone Assay

Longitudinal vector marking and overall clonal diversity. (A) Marking levels. The percentage of peripheral blood cells positive for the barcode/green fluorescent protein (GFP) vector is shown for each hematopoietic cell lineage over time for animals ZH33, ZG66, ZH19, and ZJ31. T cells (T), black; B cells (B), red; monocytes (Mono), green; granulocytes (Gr), dark blue. (B) Cumulative detected clone numbers. The cumulative number of clones over time contributing above the threshold at a minimum of 1 time point is shown for individual lineages and overall. The rapid increase in number of detected clones after engraftment corresponds to initial posttransplantation hematopoietic reconstitution with ≥1 waves of transient clones and emergence of long-term repopulating clones. The flat areas subsequent on the curves indicate broad clonal persistence and lack of emergence of new clones after initial posttransplantation reconstitution with long-term repopulating clones. These plateaus imply that capture of clones is substantially complete after several early time points. The cumulative numbers of clones detected within each individual cell type are color coded. The overall numbers of cumulatively detected clones in all cell types are plotted in gray. (C) Overall clonal diversity. Shannon entropy as a measure of diversity depends on both the number of detected clones and the distribution of their sizes. Given a number of detected clones, higher diversity corresponds to a more even distribution of sizes. Here we show that diversity is high, similar among animals, constant among cell types, and stable after initial reconstitution.
Figure Legend Snippet: Longitudinal vector marking and overall clonal diversity. (A) Marking levels. The percentage of peripheral blood cells positive for the barcode/green fluorescent protein (GFP) vector is shown for each hematopoietic cell lineage over time for animals ZH33, ZG66, ZH19, and ZJ31. T cells (T), black; B cells (B), red; monocytes (Mono), green; granulocytes (Gr), dark blue. (B) Cumulative detected clone numbers. The cumulative number of clones over time contributing above the threshold at a minimum of 1 time point is shown for individual lineages and overall. The rapid increase in number of detected clones after engraftment corresponds to initial posttransplantation hematopoietic reconstitution with ≥1 waves of transient clones and emergence of long-term repopulating clones. The flat areas subsequent on the curves indicate broad clonal persistence and lack of emergence of new clones after initial posttransplantation reconstitution with long-term repopulating clones. These plateaus imply that capture of clones is substantially complete after several early time points. The cumulative numbers of clones detected within each individual cell type are color coded. The overall numbers of cumulatively detected clones in all cell types are plotted in gray. (C) Overall clonal diversity. Shannon entropy as a measure of diversity depends on both the number of detected clones and the distribution of their sizes. Given a number of detected clones, higher diversity corresponds to a more even distribution of sizes. Here we show that diversity is high, similar among animals, constant among cell types, and stable after initial reconstitution.

Techniques Used: Plasmid Preparation, Clone Assay

23) Product Images from "A systematic comparison reveals substantial differences in chromosomal versus episomal encoding of enhancer activity"

Article Title: A systematic comparison reveals substantial differences in chromosomal versus episomal encoding of enhancer activity

Journal: Genome Research

doi: 10.1101/gr.212092.116

Study design for lentiMPRA. ( A ) Schematic diagram of lentiMPRA. Candidate enhancers and barcode tags were synthesized in tandem as a microarray-derived oligonucleotide library and cloned into the pLS-mP vector, followed by cloning of a minimal promoter
Figure Legend Snippet: Study design for lentiMPRA. ( A ) Schematic diagram of lentiMPRA. Candidate enhancers and barcode tags were synthesized in tandem as a microarray-derived oligonucleotide library and cloned into the pLS-mP vector, followed by cloning of a minimal promoter

Techniques Used: Synthesized, Microarray, Derivative Assay, Clone Assay, Plasmid Preparation

24) Product Images from "Essential roles for deubiquitination in Leishmania life cycle progression"

Article Title: Essential roles for deubiquitination in Leishmania life cycle progression

Journal: PLoS Pathogens

doi: 10.1371/journal.ppat.1008455

Life cycle phenotyping of DUB null mutants by bar-seq. Fifty-eight null mutants were pooled (n = 6) as promastigotes and grown to stationary phase before being induced to differentiate to axenic amastigotes in vitro or used for metacyclic purification and subsequent infection of macrophages or mice. At the time points indicated in (A), DNA samples were extracted for barcode amplification by PCR and quantitative analysis by next generation sequencing. Average proportional representation of null mutant-specific barcodes at each experimental stage is displayed in the heat maps for (B) axenic amastigote, (C) macrophage infection and (D) mouse infection experiments. Samples included represent promastigote time-point zero (PRO 0 h), early-log phase (PRO 24 h), mid-log phase (PRO 48 h), late-log phase (PRO 72 h), stationary phase (PRO 168 h), early axenic amastigote differentiation (AXA 24 h), post-axenic amastigote differentiation (AXA 120 h), purified metacyclic promastigotes (META), early macrophage infection (inMAC 12 h), late macrophage infection (inMAC 72 h), 3 week footpad mouse infection (FP 3) and 6 week footpad mouse infection (FP 6). Separately, individual null mutant lines were grown to stationary phase and induced to differentiate into axenic amastigotes. Cell viability was measured at the stages indicated in (E) using resazurin (added 24 h prior to each measurement). A transformation step back to promastigotes was included. Measurements of cell viability were calculated relative to the wild-type at each experimental stage. The data are an average of two independent experiments.
Figure Legend Snippet: Life cycle phenotyping of DUB null mutants by bar-seq. Fifty-eight null mutants were pooled (n = 6) as promastigotes and grown to stationary phase before being induced to differentiate to axenic amastigotes in vitro or used for metacyclic purification and subsequent infection of macrophages or mice. At the time points indicated in (A), DNA samples were extracted for barcode amplification by PCR and quantitative analysis by next generation sequencing. Average proportional representation of null mutant-specific barcodes at each experimental stage is displayed in the heat maps for (B) axenic amastigote, (C) macrophage infection and (D) mouse infection experiments. Samples included represent promastigote time-point zero (PRO 0 h), early-log phase (PRO 24 h), mid-log phase (PRO 48 h), late-log phase (PRO 72 h), stationary phase (PRO 168 h), early axenic amastigote differentiation (AXA 24 h), post-axenic amastigote differentiation (AXA 120 h), purified metacyclic promastigotes (META), early macrophage infection (inMAC 12 h), late macrophage infection (inMAC 72 h), 3 week footpad mouse infection (FP 3) and 6 week footpad mouse infection (FP 6). Separately, individual null mutant lines were grown to stationary phase and induced to differentiate into axenic amastigotes. Cell viability was measured at the stages indicated in (E) using resazurin (added 24 h prior to each measurement). A transformation step back to promastigotes was included. Measurements of cell viability were calculated relative to the wild-type at each experimental stage. The data are an average of two independent experiments.

Techniques Used: In Vitro, Purification, Infection, Mouse Assay, Amplification, Polymerase Chain Reaction, Next-Generation Sequencing, Mutagenesis, Transformation Assay

25) Product Images from "Systematic functional analysis of Leishmania protein kinases identifies regulators of differentiation or survival"

Article Title: Systematic functional analysis of Leishmania protein kinases identifies regulators of differentiation or survival

Journal: bioRxiv

doi: 10.1101/2020.09.06.279091

Identification of protein kinases involved in colonisation of Lutzomyia longipalpis . (a) Heat map of 29 protein kinase mutants that cluster in groups important for the amastigote stage in two of the three experiments using both 1 and 2 difference clustering. In bold are the genes that have been identified as important for mouse infection in a previous trypanosome study 43 . (b) Summary showing the protein kinases important for sand fly infection. Control plot (top) shows the relative barcode representation for four control lines with a barcode inserted in the ribosomal locus at three timepoints post blood meal ((PBM), Day 1, Day 5 and Day 8). Protein kinases important for L. longipalpis infection plot (bottom) shows the 15 mutants with significant loss of representation by Day 8 identified using a multiple t -test (n=2). 7 protein kinases identified as important for both sand fly infectivity and for survival in amastigote stages indicated in bold. (c) Heat map showing the amastigote stage data for the 8 protein kinase mutants that are important for sand fly infection only. (d) Plot showing the infectivity of the protein kinase mutants important for survival of amastigotes only.
Figure Legend Snippet: Identification of protein kinases involved in colonisation of Lutzomyia longipalpis . (a) Heat map of 29 protein kinase mutants that cluster in groups important for the amastigote stage in two of the three experiments using both 1 and 2 difference clustering. In bold are the genes that have been identified as important for mouse infection in a previous trypanosome study 43 . (b) Summary showing the protein kinases important for sand fly infection. Control plot (top) shows the relative barcode representation for four control lines with a barcode inserted in the ribosomal locus at three timepoints post blood meal ((PBM), Day 1, Day 5 and Day 8). Protein kinases important for L. longipalpis infection plot (bottom) shows the 15 mutants with significant loss of representation by Day 8 identified using a multiple t -test (n=2). 7 protein kinases identified as important for both sand fly infectivity and for survival in amastigote stages indicated in bold. (c) Heat map showing the amastigote stage data for the 8 protein kinase mutants that are important for sand fly infection only. (d) Plot showing the infectivity of the protein kinase mutants important for survival of amastigotes only.

Techniques Used: Infection

Generation of the L. mexicana kinome gene deletion library. (a) Schematic representation of gene editing by CRISPR-Cas9 in the L. mexicana progenitor cell line showing integration of repair cassettes containing 30nt homology sites, a unique barcode and antibiotic resistance genes for puromycin ( PAC ) and blasticidin ( BSD ). Diagnostic PCRs for the presence of the CDS with Oligo 1 and Oligo 2, (depicted in blue), correct integration of the repair cassettes using Oligo 3 and Oligo 4 (depicted in red), and the gene in the endogenous locus with Oligo 5 and Oligo 6 (depicted in green). (b) Diagnostic PCR for (i) LmxM.25.0853 and (ii) PKAC1 ( LmxM34.4010 ) using primer pairs as above in L. mexicana Cas9T7 (WT) and Populations A and B (c) Heat map indicating the presence or absence of target genes after whole genome sequencing. The ratio of gene coverage to chromosome coverage was used as a measure of gene copy number in selected mutants. A value of 1 represents two alleles of a given gene. (d) Diagnostic PCR for facilitated gene deletion mutant LmxM.04.0650 using Oligo 1 and 2 for the CDS, Oligo 5 and 6 for the endogenous locus and Oligo 3 and 4 for correct integration. Western blot using anti-myc confirmed episomal expression of LmxM.04.0650 at the anticipated size of 140 kDa.
Figure Legend Snippet: Generation of the L. mexicana kinome gene deletion library. (a) Schematic representation of gene editing by CRISPR-Cas9 in the L. mexicana progenitor cell line showing integration of repair cassettes containing 30nt homology sites, a unique barcode and antibiotic resistance genes for puromycin ( PAC ) and blasticidin ( BSD ). Diagnostic PCRs for the presence of the CDS with Oligo 1 and Oligo 2, (depicted in blue), correct integration of the repair cassettes using Oligo 3 and Oligo 4 (depicted in red), and the gene in the endogenous locus with Oligo 5 and Oligo 6 (depicted in green). (b) Diagnostic PCR for (i) LmxM.25.0853 and (ii) PKAC1 ( LmxM34.4010 ) using primer pairs as above in L. mexicana Cas9T7 (WT) and Populations A and B (c) Heat map indicating the presence or absence of target genes after whole genome sequencing. The ratio of gene coverage to chromosome coverage was used as a measure of gene copy number in selected mutants. A value of 1 represents two alleles of a given gene. (d) Diagnostic PCR for facilitated gene deletion mutant LmxM.04.0650 using Oligo 1 and 2 for the CDS, Oligo 5 and 6 for the endogenous locus and Oligo 3 and 4 for correct integration. Western blot using anti-myc confirmed episomal expression of LmxM.04.0650 at the anticipated size of 140 kDa.

Techniques Used: CRISPR, Diagnostic Assay, Polymerase Chain Reaction, Sequencing, Mutagenesis, Western Blot, Expressing

Identification of protein kinases involved in L. mexicana differentiation and infection. (a) Schematic illustration of the bar-seq screen design with three experimental arms (EA1, blue; EA2, black; EA3, red) depicting investigation of differentiation to amastigote stages and investigation of protein kinase mutants in sand fly infection (EA4, green). A pool of gene deletion mutants were generated in procyclic promastigotes (PRO), which were grown in Graces Media at pH 5.5 for 168 h. Cells were then either diluted into amastigote media at 35°C and grown as axenic amastigotes (AXA) for 5 d or enriched for metacyclic promastigotes. Metacyclic promastigotes (META) were used to either infect macrophages (inMAC) or inoculate mouse footpads (FP). DNA was taken at the indicated time points and the unique barcodes amplified by PCR to apply bar-seq analyses. (b) Proportion of barcodes across 8 time points, as indicated in A. The trajectories of six exemplar protein kinase mutants have been plotted for the three experimental arms. Values are mean +/− S.D., N = 6. (c) Projection pursuit cluster analysis was applied to the trajectories from each experimental arm, grouped into six clusters, each consisting of trajectories with a similar trend. Only clusters resulting from the mouse footpad infection are shown. The image for each cluster shows gene trajectories overlaid with average trends in bold. Trajectories plotted using logged % barcode representation data, normalised to time 0. Heat maps below show % barcode representation data and depict the trend for each individual gene. Gene IDs in red (MCA, MPK1 and MPK2) have documented phenotypes in Leishmania and serve to benchmark the dataset. (d) The relationship between the clusters is shown on a two-dimensional PCA plot. Colours match the clusters in C.
Figure Legend Snippet: Identification of protein kinases involved in L. mexicana differentiation and infection. (a) Schematic illustration of the bar-seq screen design with three experimental arms (EA1, blue; EA2, black; EA3, red) depicting investigation of differentiation to amastigote stages and investigation of protein kinase mutants in sand fly infection (EA4, green). A pool of gene deletion mutants were generated in procyclic promastigotes (PRO), which were grown in Graces Media at pH 5.5 for 168 h. Cells were then either diluted into amastigote media at 35°C and grown as axenic amastigotes (AXA) for 5 d or enriched for metacyclic promastigotes. Metacyclic promastigotes (META) were used to either infect macrophages (inMAC) or inoculate mouse footpads (FP). DNA was taken at the indicated time points and the unique barcodes amplified by PCR to apply bar-seq analyses. (b) Proportion of barcodes across 8 time points, as indicated in A. The trajectories of six exemplar protein kinase mutants have been plotted for the three experimental arms. Values are mean +/− S.D., N = 6. (c) Projection pursuit cluster analysis was applied to the trajectories from each experimental arm, grouped into six clusters, each consisting of trajectories with a similar trend. Only clusters resulting from the mouse footpad infection are shown. The image for each cluster shows gene trajectories overlaid with average trends in bold. Trajectories plotted using logged % barcode representation data, normalised to time 0. Heat maps below show % barcode representation data and depict the trend for each individual gene. Gene IDs in red (MCA, MPK1 and MPK2) have documented phenotypes in Leishmania and serve to benchmark the dataset. (d) The relationship between the clusters is shown on a two-dimensional PCA plot. Colours match the clusters in C.

Techniques Used: Infection, Generated, Amplification, Polymerase Chain Reaction

26) Product Images from "Impact of next-generation sequencing error on analysis of barcoded plasmid libraries of known complexity and sequence"

Article Title: Impact of next-generation sequencing error on analysis of barcoded plasmid libraries of known complexity and sequence

Journal: Nucleic Acids Research

doi: 10.1093/nar/gku607

Distribution of the relative abundance of the 500 most abundant barcode sequences detected following analysis of the defined barcode libraries using different sequencing platforms. Libraries containing ( A ) 1, ( B ) 10 and ( C ) 100 defined Illumina-compatible barcode(s) sequenced using the first sequencing run. For the 100-barcode library, the first 89 most abundant barcodes matched expected sequences, and a point of inflection in the distribution of the relative frequencies of barcodes occurred at the 82nd-most abundant barcode. Six putatively false barcodes that were not in the 100-barcode library were detected within the top 100. ( D ) Library containing the same 100 defined Illumina-compatible barcodes sequenced using the second sequencing run after an independent amplification. Seven putatively false barcodes were detected within the top 100. A point of inflection occurred at the 79th-most abundant barcode and again, the first 89 most abundant barcodes matched expected sequences. ( E ) Library containing 100 defined SOLiD-compatible barcodes. The first 82 most abundant barcodes matched expected sequences; however, 13 putatively false barcodes were detected in the top 100. ( F ) Mean and range of relative abundances of expected and false barcodes, for each sample.
Figure Legend Snippet: Distribution of the relative abundance of the 500 most abundant barcode sequences detected following analysis of the defined barcode libraries using different sequencing platforms. Libraries containing ( A ) 1, ( B ) 10 and ( C ) 100 defined Illumina-compatible barcode(s) sequenced using the first sequencing run. For the 100-barcode library, the first 89 most abundant barcodes matched expected sequences, and a point of inflection in the distribution of the relative frequencies of barcodes occurred at the 82nd-most abundant barcode. Six putatively false barcodes that were not in the 100-barcode library were detected within the top 100. ( D ) Library containing the same 100 defined Illumina-compatible barcodes sequenced using the second sequencing run after an independent amplification. Seven putatively false barcodes were detected within the top 100. A point of inflection occurred at the 79th-most abundant barcode and again, the first 89 most abundant barcodes matched expected sequences. ( E ) Library containing 100 defined SOLiD-compatible barcodes. The first 82 most abundant barcodes matched expected sequences; however, 13 putatively false barcodes were detected in the top 100. ( F ) Mean and range of relative abundances of expected and false barcodes, for each sample.

Techniques Used: Sequencing, Amplification

Experimental design and analytical workflow for analysis of the Illumina-compatible barcode. ( A ) Structure and sequence of the Illumina-compatible barcode insert cloned into the NsiI site of the pEF1α.γc lentiviral construct. The insert contained a PstI site, 32 bp of the Illumina adaptor sequence, a 16-bp random sequence that functioned as the lentiviral barcode and an 18-bp known sequence. Numbers indicate the position of every fifth random nucleotide in the barcode. The SOLiD-compatible barcode followed a similar configuration, with the insert containing a PstI site, 23 bp of the P1-T adaptor, a 15-bp random sequence for the lentiviral barcode and the internal adaptor. For both barcode configurations, the barcode regions were amplified with 10 PCR cycles using primers that introduced the adaptor sequences required for the Illumina or SOLiD platforms. ( B ) Strategy for analyzing sequence data for the Illumina-compatible barcode. Raw sequence reads were filtered using the known sequence immediately following the barcode at positions 17–30 to eliminate indel errors. The lentiviral barcode was trimmed to positions 2–16 to avoid errors at position 1. The number of unique barcode sequences was counted with and without phred score filtering (Q30), and with and without allowing one mismatch. For the SOLiD-compatible barcode, raw sequence reads were filtered using 10 internal adaptor sequences and the number of unique barcode sequences were counted with and without allowing one mismatch.
Figure Legend Snippet: Experimental design and analytical workflow for analysis of the Illumina-compatible barcode. ( A ) Structure and sequence of the Illumina-compatible barcode insert cloned into the NsiI site of the pEF1α.γc lentiviral construct. The insert contained a PstI site, 32 bp of the Illumina adaptor sequence, a 16-bp random sequence that functioned as the lentiviral barcode and an 18-bp known sequence. Numbers indicate the position of every fifth random nucleotide in the barcode. The SOLiD-compatible barcode followed a similar configuration, with the insert containing a PstI site, 23 bp of the P1-T adaptor, a 15-bp random sequence for the lentiviral barcode and the internal adaptor. For both barcode configurations, the barcode regions were amplified with 10 PCR cycles using primers that introduced the adaptor sequences required for the Illumina or SOLiD platforms. ( B ) Strategy for analyzing sequence data for the Illumina-compatible barcode. Raw sequence reads were filtered using the known sequence immediately following the barcode at positions 17–30 to eliminate indel errors. The lentiviral barcode was trimmed to positions 2–16 to avoid errors at position 1. The number of unique barcode sequences was counted with and without phred score filtering (Q30), and with and without allowing one mismatch. For the SOLiD-compatible barcode, raw sequence reads were filtered using 10 internal adaptor sequences and the number of unique barcode sequences were counted with and without allowing one mismatch.

Techniques Used: Sequencing, Clone Assay, Construct, Amplification, Polymerase Chain Reaction

Analysis of the relative abundance, GC content and likelihood of secondary structure formation for each of the 100 expected Illumina-compatible barcode sequences. ( A ) Relative abundance of the 100 expected barcode sequences, as detected during the first and second sequencing runs using the Illumina HiSeq 2000 (Pearson r (98) = 0.93, p
Figure Legend Snippet: Analysis of the relative abundance, GC content and likelihood of secondary structure formation for each of the 100 expected Illumina-compatible barcode sequences. ( A ) Relative abundance of the 100 expected barcode sequences, as detected during the first and second sequencing runs using the Illumina HiSeq 2000 (Pearson r (98) = 0.93, p

Techniques Used: Sequencing

Analysis of the position and substitution-like type of error for all one-mismatch sequence errors for both Illumina HiSeq 2000 sequencing runs. One-mismatch errors were compared to the known barcode sequences from which they were derived. Errors from the first sequencing run represent the sum of one-mismatch errors after Q30 quality filtering for the one-barcode sample and 10- and 100-barcode libraries, although one-mismatch errors from the 100-barcode library comprise 95.3% of all errors. Errors from the second sequencing run represent one-mismatch errors after Q30 quality filtering for the 100-barcode library. ( A ) Distribution of one-mismatch errors across each position of the barcode (positions 2–16 of the sequence reads). This distribution differed significantly from an expected even distribution (χ 2 = 30 064, df = 14, p
Figure Legend Snippet: Analysis of the position and substitution-like type of error for all one-mismatch sequence errors for both Illumina HiSeq 2000 sequencing runs. One-mismatch errors were compared to the known barcode sequences from which they were derived. Errors from the first sequencing run represent the sum of one-mismatch errors after Q30 quality filtering for the one-barcode sample and 10- and 100-barcode libraries, although one-mismatch errors from the 100-barcode library comprise 95.3% of all errors. Errors from the second sequencing run represent one-mismatch errors after Q30 quality filtering for the 100-barcode library. ( A ) Distribution of one-mismatch errors across each position of the barcode (positions 2–16 of the sequence reads). This distribution differed significantly from an expected even distribution (χ 2 = 30 064, df = 14, p

Techniques Used: Sequencing, Derivative Assay

27) Product Images from "SunCatcher: Clonal Barcoding with qPCR-Based Detection Enables Functional Analysis of Live Cells and Generation of Custom Combinations of Cells for Research and Discovery"

Article Title: SunCatcher: Clonal Barcoding with qPCR-Based Detection Enables Functional Analysis of Live Cells and Generation of Custom Combinations of Cells for Research and Discovery

Journal: bioRxiv

doi: 10.1101/2021.10.13.464251

SunCatcher Enables Analysis of Clonal Dynamics During Primary Tumor Progression. (A) Quantitative PCR assessment of barcode composition in the Met1 BC Pool at time of injection (left), in each of 10 tumors (n=5 mice) after 18 days (center), and calculation of the average composition of all tumors (right). Composition bars show indicated barcodes as a percent of total barcode signal (100%) within each sample; tumor mass (mg) is indicated above each bar. Color code for each BC is indicated. (B, C) Average representation of each BC in n=10 tumors for each of 2 experiments. BCs that constituted > 0.5% of total BC signal are represented in (B) and
Figure Legend Snippet: SunCatcher Enables Analysis of Clonal Dynamics During Primary Tumor Progression. (A) Quantitative PCR assessment of barcode composition in the Met1 BC Pool at time of injection (left), in each of 10 tumors (n=5 mice) after 18 days (center), and calculation of the average composition of all tumors (right). Composition bars show indicated barcodes as a percent of total barcode signal (100%) within each sample; tumor mass (mg) is indicated above each bar. Color code for each BC is indicated. (B, C) Average representation of each BC in n=10 tumors for each of 2 experiments. BCs that constituted > 0.5% of total BC signal are represented in (B) and

Techniques Used: Real-time Polymerase Chain Reaction, Injection, Mouse Assay

BC Detection by Next-Generation Sequencing and qPCR-Based Methods. ( A ) Multi-level multiplexing is achieved by attaching 1 of 20 unique barcode-indexes to the 3’ end of each sample. Up to 20 indexed samples can be pooled into a single barcode-index library. Each barcode-index library can be ligated into 1 of 24 uniquely indexed Illumina adaptors. Purple region represents PstI, light blue represents MluI. ( B ) Sequencing read counts for 2 test samples using the Illumina library preparation method. Each library corresponds to a single Illumina adaptor, and the expected barcode pair for each library is indicated. ( C ) Design of pre-amplification primers (red arrows) and BC-specific primers (black arrows). ( D ) Heatmap of qPCR C T values when testing each indicated barcode oligonucleotide primer against every Met1 BC population as well as the Met1-BC pool. ( E ) Genomic DNA was isolated from 6 tumors derived from the McNeu BC Pool after ∼5 weeks of tumor growth in vivo; each barcode was quantified as a percentage of total barcodes in a given tumor by both qPCR and next-generation sequencing. Each datapoint represents the value for an individual barcode from an individual tumor by each method.
Figure Legend Snippet: BC Detection by Next-Generation Sequencing and qPCR-Based Methods. ( A ) Multi-level multiplexing is achieved by attaching 1 of 20 unique barcode-indexes to the 3’ end of each sample. Up to 20 indexed samples can be pooled into a single barcode-index library. Each barcode-index library can be ligated into 1 of 24 uniquely indexed Illumina adaptors. Purple region represents PstI, light blue represents MluI. ( B ) Sequencing read counts for 2 test samples using the Illumina library preparation method. Each library corresponds to a single Illumina adaptor, and the expected barcode pair for each library is indicated. ( C ) Design of pre-amplification primers (red arrows) and BC-specific primers (black arrows). ( D ) Heatmap of qPCR C T values when testing each indicated barcode oligonucleotide primer against every Met1 BC population as well as the Met1-BC pool. ( E ) Genomic DNA was isolated from 6 tumors derived from the McNeu BC Pool after ∼5 weeks of tumor growth in vivo; each barcode was quantified as a percentage of total barcodes in a given tumor by both qPCR and next-generation sequencing. Each datapoint represents the value for an individual barcode from an individual tumor by each method.

Techniques Used: Next-Generation Sequencing, Real-time Polymerase Chain Reaction, Multiplexing, Sequencing, Amplification, Isolation, Derivative Assay, In Vivo

SunCatcher Clonal Barcoding and Functional Analyses. ( A ) Schematic of the SunCatcher Clonal Barcoding Method. SunCatcher utilizes two rounds of single cell cloning to ensure that each subclone has only 1 unique barcode and that each cell within that subclone contains the same barcode insertion site. Due to the single cell cloning approach, custom BC pools of any combination can be designed. (FACS, fluorescence-activated cell sorting; NBC, non-barcoded clone; BPP, barcoded polyclonal population (polyclonal for barcode insertion site); BC, barcoded clone; BC Pool, population containing multiple BCs). ( B ) BCs or BC Pools (input) can be entered into any experiment and detected at end point (output) using various deconvolution approaches. Deconvolution by qPCR provides a cost effective, rapid, and highly sensitive method for detecting and quantifying BCs. SunCatcher enables retrieval of all clones, including those negatively selected during experimentation, for further analyses and/or design and testing of custom BC Pools.
Figure Legend Snippet: SunCatcher Clonal Barcoding and Functional Analyses. ( A ) Schematic of the SunCatcher Clonal Barcoding Method. SunCatcher utilizes two rounds of single cell cloning to ensure that each subclone has only 1 unique barcode and that each cell within that subclone contains the same barcode insertion site. Due to the single cell cloning approach, custom BC pools of any combination can be designed. (FACS, fluorescence-activated cell sorting; NBC, non-barcoded clone; BPP, barcoded polyclonal population (polyclonal for barcode insertion site); BC, barcoded clone; BC Pool, population containing multiple BCs). ( B ) BCs or BC Pools (input) can be entered into any experiment and detected at end point (output) using various deconvolution approaches. Deconvolution by qPCR provides a cost effective, rapid, and highly sensitive method for detecting and quantifying BCs. SunCatcher enables retrieval of all clones, including those negatively selected during experimentation, for further analyses and/or design and testing of custom BC Pools.

Techniques Used: Functional Assay, Clone Assay, FACS, Fluorescence, Real-time Polymerase Chain Reaction

SunCatcher Barcoding to Study Metastasis. (A) Calibration were curves generated for indicated tissues by serially diluting known amounts of barcoded tumor cell gDNA into a fixed amount of normal tissue gDNA. From top to bottom: lung, long bones (from femur and tibia), mandible. (B) 2.5×10^5 barcoded Met1 tumor cells were injected bilaterally into the mammary fat pads (n=5 animals) and tumors were allowed to grow for 21 days, at which point tissues were harvested and metastasis burden was calculated. Dashed lines indicate the background signals from each indicated tissue type. Tissues with signal above the background were considered as positive for metastasis and estimated tumor cell number per 0.1mg tissue was calculated based on the calibration curve for that specific tissue. (C) Barcode composition analysis on tissues with positive metastasis signal. Bars represent percent of total barcode signal (100%) within each sample. Also shown are mouse identities, total primary tumor burden for each animal, and estimated numbers of metastases per tissue; N.D., not detected.
Figure Legend Snippet: SunCatcher Barcoding to Study Metastasis. (A) Calibration were curves generated for indicated tissues by serially diluting known amounts of barcoded tumor cell gDNA into a fixed amount of normal tissue gDNA. From top to bottom: lung, long bones (from femur and tibia), mandible. (B) 2.5×10^5 barcoded Met1 tumor cells were injected bilaterally into the mammary fat pads (n=5 animals) and tumors were allowed to grow for 21 days, at which point tissues were harvested and metastasis burden was calculated. Dashed lines indicate the background signals from each indicated tissue type. Tissues with signal above the background were considered as positive for metastasis and estimated tumor cell number per 0.1mg tissue was calculated based on the calibration curve for that specific tissue. (C) Barcode composition analysis on tissues with positive metastasis signal. Bars represent percent of total barcode signal (100%) within each sample. Also shown are mouse identities, total primary tumor burden for each animal, and estimated numbers of metastases per tissue; N.D., not detected.

Techniques Used: Generated, Injection

28) Product Images from "Identification and Massively Parallel Characterization of Regulatory Elements Driving Neural Induction"

Article Title: Identification and Massively Parallel Characterization of Regulatory Elements Driving Neural Induction

Journal: Cell stem cell

doi: 10.1016/j.stem.2019.09.010

Experimental Design of lentiMPRA ), 193 positive control regions, and 200 negative controls were included as well. (B) Schematic showing lentiMPRA design. CRSs along with 15-bp barcodes were synthesized on a custom array and cloned into a lentiMPRA vector. The library was packaged into lentivirus and infected into hESCs. The infected cells were cultured for 3 days to allow genomic integration. DNA and nuclear RNA were extracted at seven time points (0, 3, 6, 12, 24, 48, and 72 h) and subjected to sequencing followed by estimation of transcriptional activity. ARE, antirepressor element; BC, barcode; LTR, long terminal repeat; mP, minimal promoter; WPRE, woodchuck hepatitis virus posttranscriptional regulatory element.
Figure Legend Snippet: Experimental Design of lentiMPRA ), 193 positive control regions, and 200 negative controls were included as well. (B) Schematic showing lentiMPRA design. CRSs along with 15-bp barcodes were synthesized on a custom array and cloned into a lentiMPRA vector. The library was packaged into lentivirus and infected into hESCs. The infected cells were cultured for 3 days to allow genomic integration. DNA and nuclear RNA were extracted at seven time points (0, 3, 6, 12, 24, 48, and 72 h) and subjected to sequencing followed by estimation of transcriptional activity. ARE, antirepressor element; BC, barcode; LTR, long terminal repeat; mP, minimal promoter; WPRE, woodchuck hepatitis virus posttranscriptional regulatory element.

Techniques Used: Positive Control, Synthesized, Clone Assay, Plasmid Preparation, Infection, Cell Culture, Sequencing, Activity Assay

29) Product Images from "High-throughput detection of eukaryotic parasites and arboviruses in mosquitoes"

Article Title: High-throughput detection of eukaryotic parasites and arboviruses in mosquitoes

Journal: Biology Open

doi: 10.1242/bio.058855

Overview of the sequencing-based assay. To create libraries for amplicon sequencing, we amplify each sample separately with tailed primers targeting each group of interest ( Table 1 ). We then pool amplicons from each PCR by sample and perform a second amplification to incorporate a sample barcode and the Illumina adapter sequences. After the barcoding PCR, we pool all samples together before sequencing.
Figure Legend Snippet: Overview of the sequencing-based assay. To create libraries for amplicon sequencing, we amplify each sample separately with tailed primers targeting each group of interest ( Table 1 ). We then pool amplicons from each PCR by sample and perform a second amplification to incorporate a sample barcode and the Illumina adapter sequences. After the barcoding PCR, we pool all samples together before sequencing.

Techniques Used: Sequencing, Amplification, Polymerase Chain Reaction

30) Product Images from "Detection and removal of barcode swapping in single-cell RNA-seq data"

Article Title: Detection and removal of barcode swapping in single-cell RNA-seq data

Journal: bioRxiv

doi: 10.1101/177048

Characterization of barcode swapping in droplet-based scRNA-seq experiments. ( A ) The expected number of cells with shared cell barcodes in 10X samples that have been multiplexed for sequencing, for different numbers of samples and different numbers of captured cells per sample. ( B ) A schematic of our method to remove swapped reads from droplet data. Reads found in different samples with the same combination of UMI, cell barcode, and aligned gene were considered to have swapped. If most reads (≥ 80%) were present in one sample, we excluded the molecule from all other samples (i). If reads were more evenly spread across samples, we excluded the molecule from all samples (ii). Reads in one sample only were retained (iii). ( C ) t -SNE plot of the expression profiles of mouse epithelial cells ( Maaten Hinton, 2008 ). Each point represents a cell that is coloured by sample. Letters correspond to different experimental conditions while numbers represent biological replicates. ( D ) The distribution of the library sizes for called cells in each sample. Cells were called using emptyDrops ( Lun, 2018 ), with an FDR threshold of 1% and a minimum of 1,000 UMIs. ( E ) The number of called cells for each sample, before and after application of our swapped read exclusion algorithm.
Figure Legend Snippet: Characterization of barcode swapping in droplet-based scRNA-seq experiments. ( A ) The expected number of cells with shared cell barcodes in 10X samples that have been multiplexed for sequencing, for different numbers of samples and different numbers of captured cells per sample. ( B ) A schematic of our method to remove swapped reads from droplet data. Reads found in different samples with the same combination of UMI, cell barcode, and aligned gene were considered to have swapped. If most reads (≥ 80%) were present in one sample, we excluded the molecule from all other samples (i). If reads were more evenly spread across samples, we excluded the molecule from all samples (ii). Reads in one sample only were retained (iii). ( C ) t -SNE plot of the expression profiles of mouse epithelial cells ( Maaten Hinton, 2008 ). Each point represents a cell that is coloured by sample. Letters correspond to different experimental conditions while numbers represent biological replicates. ( D ) The distribution of the library sizes for called cells in each sample. Cells were called using emptyDrops ( Lun, 2018 ), with an FDR threshold of 1% and a minimum of 1,000 UMIs. ( E ) The number of called cells for each sample, before and after application of our swapped read exclusion algorithm.

Techniques Used: Sequencing, Expressing

A schematic of the mechanism for barcode swapping, as proposed by Sinha et al. (2017) . On new models of the Illumina sequencing machines, flow cell seeding and DNA amplification take place simultaneously, without any washes of the flow cell between steps. As a result, free sample indexing barcodes remain in solution and can be inadvertently extended using DNA molecules from libraries with different barcodes as templates. The transfer of mislabeled molecules between nanowells of the flow cell results in clustering and sequencing of artefactually labelled DNA molecules.
Figure Legend Snippet: A schematic of the mechanism for barcode swapping, as proposed by Sinha et al. (2017) . On new models of the Illumina sequencing machines, flow cell seeding and DNA amplification take place simultaneously, without any washes of the flow cell between steps. As a result, free sample indexing barcodes remain in solution and can be inadvertently extended using DNA molecules from libraries with different barcodes as templates. The transfer of mislabeled molecules between nanowells of the flow cell results in clustering and sequencing of artefactually labelled DNA molecules.

Techniques Used: Sequencing, Amplification

Characterization of barcode swapping in plate-based scRNA-seq experiments. ( A ) The experimental design of the Richard dataset. Two 96-well plates of cells were multiplexed for sequencing. Expected barcode combinations are marked in blue, while impossible barcode combinations are marked in grey. ( B ) Distribution of the library sizes (i.e., number of mapped reads) in the expected and impossible barcode combinations. ( C ) Library size of each impossible combination (observed swapped reads), plotted against the sum of the library sizes of the expected combinations that share exactly one barcode with that impossible combination (available swapped reads). An example is illustrated graphically in the inset Figure for one impossible combination (red) and the contributing expected combinations (orange). The gradient represents the fraction of available reads from the expected combinations that swap into each impossible combination. ( D ) Estimated swapping fractions for different plates of the Nestorowa et al. (2016) dataset, plotted against the ratio of the concentration of free barcode to the concentration of cDNA of the correct length for sequencing. A linear regression fit is shown along with its 95% confidence interval. The slope of the fitted line is not significantly different from 0 ( p = 0.129).
Figure Legend Snippet: Characterization of barcode swapping in plate-based scRNA-seq experiments. ( A ) The experimental design of the Richard dataset. Two 96-well plates of cells were multiplexed for sequencing. Expected barcode combinations are marked in blue, while impossible barcode combinations are marked in grey. ( B ) Distribution of the library sizes (i.e., number of mapped reads) in the expected and impossible barcode combinations. ( C ) Library size of each impossible combination (observed swapped reads), plotted against the sum of the library sizes of the expected combinations that share exactly one barcode with that impossible combination (available swapped reads). An example is illustrated graphically in the inset Figure for one impossible combination (red) and the contributing expected combinations (orange). The gradient represents the fraction of available reads from the expected combinations that swap into each impossible combination. ( D ) Estimated swapping fractions for different plates of the Nestorowa et al. (2016) dataset, plotted against the ratio of the concentration of free barcode to the concentration of cDNA of the correct length for sequencing. A linear regression fit is shown along with its 95% confidence interval. The slope of the fitted line is not significantly different from 0 ( p = 0.129).

Techniques Used: Sequencing, Concentration Assay

31) Product Images from "The cis-regulatory effects of modern human-specific variants"

Article Title: The cis-regulatory effects of modern human-specific variants

Journal: bioRxiv

doi: 10.1101/2020.10.07.330761

Reproducibility of lentiMPRA data. a. Distribution of number of barcodes per each sequence. b. Replicate-by-replicate correlation of expression (RNA/DNA). Each point represents an active sequence. c. Simulations of barcode down-sampling showing Pearson’s correlation of expression (RNA/DNA) between replicates. Upper panel shows all sequences and lower panel shows sequences with higher expression (RNA/DNA > 3). Pearson’s r values are normalized to maximum Pearson’s r observed for each pair of replicates. d. Box plots of scrambled, positive control, inactive and active sequences. One-sided t -test P -values are shown. Boxes show interquartile range (IQR), black line within box shows median, whiskers show 1.5xIQR from box borders, points show outliers.
Figure Legend Snippet: Reproducibility of lentiMPRA data. a. Distribution of number of barcodes per each sequence. b. Replicate-by-replicate correlation of expression (RNA/DNA). Each point represents an active sequence. c. Simulations of barcode down-sampling showing Pearson’s correlation of expression (RNA/DNA) between replicates. Upper panel shows all sequences and lower panel shows sequences with higher expression (RNA/DNA > 3). Pearson’s r values are normalized to maximum Pearson’s r observed for each pair of replicates. d. Box plots of scrambled, positive control, inactive and active sequences. One-sided t -test P -values are shown. Boxes show interquartile range (IQR), black line within box shows median, whiskers show 1.5xIQR from box borders, points show outliers.

Techniques Used: Sequencing, Expressing, Sampling, Positive Control

32) Product Images from "Deep mutational scans for ACE2 binding, RBD expression, and antibody escape in the SARS-CoV-2 Omicron BA.1 and BA.2 receptor-binding domains"

Article Title: Deep mutational scans for ACE2 binding, RBD expression, and antibody escape in the SARS-CoV-2 Omicron BA.1 and BA.2 receptor-binding domains

Journal: bioRxiv

doi: 10.1101/2022.09.20.508745

Mutant library generation and statistics. (A) Scheme for generation of the Omicron BA.1 and BA.2 RBD mutant libraries. Site saturation mutagenesis oligonucleotide libraries were constructed by Twist Bioscience with constant flank sequences. For each Omicron background, a three-fragment Gibson Assembly was performed with: (1) the pooled mutant RBD oligonucleotide, (2) a PCR-generated oligonucleotide encoding a randomized N16 nucleotide barcode, and (3) linearized vector backbone. PacBio sequencing of the barcoded mutant library plasmid was used to link N16 barcode to mutant RBD sequence, enabling complete definition of library statistics and the creation of a barcode-variant lookup table such that subsequent deep mutational scans only require N16 barcode sequencing. (B-E) For pooled duplicate Omicron BA.1 (top) and BA.2 (bottom) libraries, (B) the average number of mutations of each class per barcoded variant, (C) the distribution of the number of amino acid mutations per barcoded variant, (D) the mutation rate at each site along the RBD sequence, and (E) the distribution of the total number of associated N16 barcodes for each possible amino acid mutation (from filtered ACE2 binding scores).
Figure Legend Snippet: Mutant library generation and statistics. (A) Scheme for generation of the Omicron BA.1 and BA.2 RBD mutant libraries. Site saturation mutagenesis oligonucleotide libraries were constructed by Twist Bioscience with constant flank sequences. For each Omicron background, a three-fragment Gibson Assembly was performed with: (1) the pooled mutant RBD oligonucleotide, (2) a PCR-generated oligonucleotide encoding a randomized N16 nucleotide barcode, and (3) linearized vector backbone. PacBio sequencing of the barcoded mutant library plasmid was used to link N16 barcode to mutant RBD sequence, enabling complete definition of library statistics and the creation of a barcode-variant lookup table such that subsequent deep mutational scans only require N16 barcode sequencing. (B-E) For pooled duplicate Omicron BA.1 (top) and BA.2 (bottom) libraries, (B) the average number of mutations of each class per barcoded variant, (C) the distribution of the number of amino acid mutations per barcoded variant, (D) the mutation rate at each site along the RBD sequence, and (E) the distribution of the total number of associated N16 barcodes for each possible amino acid mutation (from filtered ACE2 binding scores).

Techniques Used: Mutagenesis, Construct, Polymerase Chain Reaction, Generated, Plasmid Preparation, Sequencing, Variant Assay, Binding Assay

33) Product Images from "Rapid, large-scale species discovery in hyperdiverse taxa using 1D MinION sequencing"

Article Title: Rapid, large-scale species discovery in hyperdiverse taxa using 1D MinION sequencing

Journal: BMC Biology

doi: 10.1186/s12915-019-0706-9

Ambiguities in MAFFT+AA (purple), RACON+AA (yellow), and consolidated barcodes (green) with varying namino parameters (1, 2, and 3). One outlier value for Racon+3AA barcode was excluded from the plot. The plot shows that the consolidated barcodes have few ambiguities remaining
Figure Legend Snippet: Ambiguities in MAFFT+AA (purple), RACON+AA (yellow), and consolidated barcodes (green) with varying namino parameters (1, 2, and 3). One outlier value for Racon+3AA barcode was excluded from the plot. The plot shows that the consolidated barcodes have few ambiguities remaining

Techniques Used:

34) Product Images from "A Robust, Simple Genotyping-by-Sequencing (GBS) Approach for High Diversity Species"

Article Title: A Robust, Simple Genotyping-by-Sequencing (GBS) Approach for High Diversity Species

Journal: PLoS ONE

doi: 10.1371/journal.pone.0019379

GBS adapters, PCR and sequencing primers. (a) Sequences of double-stranded barcode and common adapters. Adapters are shown ligated to Ape KI-cut genomic DNA. Positions of the barcode sequence and Ape KI overhangs are shown relative to the insert DNA; (b) Sequences of PCR primer 1 and paired end sequencing primer 1 (PE-1). Binding sites for flowcell oligonucleotide 1 and barcode adapter are indicated; (c) Sequences of PCR primer 2 and paired end sequencing primer 2 (PE-2). Binding sites for flowcell oligonucleotide 2 and common adapter are indicated.
Figure Legend Snippet: GBS adapters, PCR and sequencing primers. (a) Sequences of double-stranded barcode and common adapters. Adapters are shown ligated to Ape KI-cut genomic DNA. Positions of the barcode sequence and Ape KI overhangs are shown relative to the insert DNA; (b) Sequences of PCR primer 1 and paired end sequencing primer 1 (PE-1). Binding sites for flowcell oligonucleotide 1 and barcode adapter are indicated; (c) Sequences of PCR primer 2 and paired end sequencing primer 2 (PE-2). Binding sites for flowcell oligonucleotide 2 and common adapter are indicated.

Techniques Used: Polymerase Chain Reaction, Sequencing, Binding Assay

Steps in GBS library construction. Note: Up to 96 DNA samples can be processed simultaneously. (1) DNA samples, barcode, and common adapter pairs are plated and dried; (2–3) samples are then digested with Ape KI and adapters are ligated to the ends of genomic DNA fragments; (4) T4 ligase is inactivated by heating and an aliquot of each sample is pooled and applied to a size exclusion column to remove unreacted adapters; (5) appropriate primers with binding sites on the ligated adapters are added and PCR is performed to increase the fragment pool; (6–7) PCR products are cleaned up and fragment sizes of the resulting library are checked on a DNA analyzer(BioRad Experion® or similar instrument). Libraries without adapter dimers are retained for DNA sequencing.
Figure Legend Snippet: Steps in GBS library construction. Note: Up to 96 DNA samples can be processed simultaneously. (1) DNA samples, barcode, and common adapter pairs are plated and dried; (2–3) samples are then digested with Ape KI and adapters are ligated to the ends of genomic DNA fragments; (4) T4 ligase is inactivated by heating and an aliquot of each sample is pooled and applied to a size exclusion column to remove unreacted adapters; (5) appropriate primers with binding sites on the ligated adapters are added and PCR is performed to increase the fragment pool; (6–7) PCR products are cleaned up and fragment sizes of the resulting library are checked on a DNA analyzer(BioRad Experion® or similar instrument). Libraries without adapter dimers are retained for DNA sequencing.

Techniques Used: Binding Assay, Polymerase Chain Reaction, DNA Sequencing

35) Product Images from "REMBRANDT: A high-throughput barcoded sequencing approach for COVID-19 screening"

Article Title: REMBRANDT: A high-throughput barcoded sequencing approach for COVID-19 screening

Journal: bioRxiv

doi: 10.1101/2020.05.16.099747

Isothermal Amplification of SARS-CoV-2 and RNase P RNAs. (A) Agarose gel of the T7 PCR products of a region of the SARS-CoV-2 N gene and human RNAse P. (B) Agarose gel of the isothermal amplification of human RNAse P from purified RNA or control (water, -). (C) Agarose gel of the isothermal amplification of SARS-CoV2-N gene from purified RNA or control (water, -). (D) Agarose gels using 8 different forward and 12 different reverse barcoded primers for the isothermal amplification of the SARS-CoV-2 N gene. Numbered lanes represent unique barcode combinations; – represents water control for each primer pair.
Figure Legend Snippet: Isothermal Amplification of SARS-CoV-2 and RNase P RNAs. (A) Agarose gel of the T7 PCR products of a region of the SARS-CoV-2 N gene and human RNAse P. (B) Agarose gel of the isothermal amplification of human RNAse P from purified RNA or control (water, -). (C) Agarose gel of the isothermal amplification of SARS-CoV2-N gene from purified RNA or control (water, -). (D) Agarose gels using 8 different forward and 12 different reverse barcoded primers for the isothermal amplification of the SARS-CoV-2 N gene. Numbered lanes represent unique barcode combinations; – represents water control for each primer pair.

Techniques Used: Amplification, Agarose Gel Electrophoresis, Polymerase Chain Reaction, Purification

The computational framework for barcode and gene identification. Schematic detailing the protocol utilized for the bioinformatics analysis of the REMBRANDT RNA-seq data.
Figure Legend Snippet: The computational framework for barcode and gene identification. Schematic detailing the protocol utilized for the bioinformatics analysis of the REMBRANDT RNA-seq data.

Techniques Used: RNA Sequencing Assay

36) Product Images from "Clonal tracking of erythropoiesis in rhesus macaques"

Article Title: Clonal tracking of erythropoiesis in rhesus macaques

Journal: Haematologica

doi: 10.3324/haematol.2019.231811

Clonal contributions to circulating erythrocytes. (A) Flowchart for RNA barcode retrieval from circulating anucleate mature red blood cells (RBC) and reticulocytes. (B) FACS plots (left panel) showing CD45 expression on whole blood cells before and after leukocyte depletion, and a Wright’s stained blood smear post leukocyte depletion (right panel). The red arrows indicate polychromatophilic reticulocytes with a blue-gray color due to increased RNA content. (C) Heatmap plotting contributions from the top 30 clones in each sample of DNA or RNA obtained from ZK22 15.5m post transplantation, plotted across all samples; heatmap was made as explained in Figure 2B . The paired Pearson correlations between DNA and RNA global barcode contributions to the same sample are given on the bottom of the heatmap. (D) The heatmaps (upper panel) and Pearson correlation plots (lower panel) show peripheral blood (PB) RBC RNA barcodes and the DNA barcode from PB Mono and granulocyte (Gr) and BM Mono, Gr, nucleated red blood cell (NRBC) at the same time point from three rhesus macaque (RM). The color scale is on the right of each panel.
Figure Legend Snippet: Clonal contributions to circulating erythrocytes. (A) Flowchart for RNA barcode retrieval from circulating anucleate mature red blood cells (RBC) and reticulocytes. (B) FACS plots (left panel) showing CD45 expression on whole blood cells before and after leukocyte depletion, and a Wright’s stained blood smear post leukocyte depletion (right panel). The red arrows indicate polychromatophilic reticulocytes with a blue-gray color due to increased RNA content. (C) Heatmap plotting contributions from the top 30 clones in each sample of DNA or RNA obtained from ZK22 15.5m post transplantation, plotted across all samples; heatmap was made as explained in Figure 2B . The paired Pearson correlations between DNA and RNA global barcode contributions to the same sample are given on the bottom of the heatmap. (D) The heatmaps (upper panel) and Pearson correlation plots (lower panel) show peripheral blood (PB) RBC RNA barcodes and the DNA barcode from PB Mono and granulocyte (Gr) and BM Mono, Gr, nucleated red blood cell (NRBC) at the same time point from three rhesus macaque (RM). The color scale is on the right of each panel.

Techniques Used: FACS, Expressing, Staining, Clone Assay, Transplantation Assay

Stability of erythroid clonal contributions over time. Heatmaps plotting barcode contributions to peripheral blood (PB) red blood cell (RBC) RNA over time in five young macaques and one aged (RQ3600) macaque, compared to DNA barcode from PB monocytes (Mono) and granulocytes (Gr). Each heatmap plots the top ten contributing clones in each sample across all samples, heatmaps were made as explained in Figure 2B . The color scale is on the right.
Figure Legend Snippet: Stability of erythroid clonal contributions over time. Heatmaps plotting barcode contributions to peripheral blood (PB) red blood cell (RBC) RNA over time in five young macaques and one aged (RQ3600) macaque, compared to DNA barcode from PB monocytes (Mono) and granulocytes (Gr). Each heatmap plots the top ten contributing clones in each sample across all samples, heatmaps were made as explained in Figure 2B . The color scale is on the right.

Techniques Used: Clone Assay

Barcode analysis of bone marrow (BM) colony-forming units (CFU). (A) Flowchart for CFU collection and barcode retrieval. (B) Heatmap of top 30 clones in BM CD34 + cells, T, B, Mono, Gr and nucleated red blood cell (NRBC), and pooled myeloid and erythroid CFU samples from ZJ31 (3.5m) and JD76 (3.5m). The colony number of CFU-E and CFU-GM pooled for DNA extraction and analysis are given on top of each CFU column. Heatmaps were constructed as described in Figure 2B .
Figure Legend Snippet: Barcode analysis of bone marrow (BM) colony-forming units (CFU). (A) Flowchart for CFU collection and barcode retrieval. (B) Heatmap of top 30 clones in BM CD34 + cells, T, B, Mono, Gr and nucleated red blood cell (NRBC), and pooled myeloid and erythroid CFU samples from ZJ31 (3.5m) and JD76 (3.5m). The colony number of CFU-E and CFU-GM pooled for DNA extraction and analysis are given on top of each CFU column. Heatmaps were constructed as described in Figure 2B .

Techniques Used: Clone Assay, DNA Extraction, Construct

Experimental design. Oligonucleotides consisting of a 6bp library ID followed by a 27-35bp high diversity random sequence barcode were inserted into a lentiviral vector flanked by polymerase chain reaction (PCR) primer sites. RM CD34 + hematopoietic stem and progenitor cell (HSPC) were mobilized into the peripheral blood (PB), collected by apheresis, enriched via immunoselection, transduced with the barcoded lentiviral library, and infused back into the total body irradiation (TBI) irradiated autologous macaque. After engraftment, PB and bone marrow (BM) samples were obtained, and various hematopoietic lineages were purified for barcode retrieval and analyses. Lineage cells and nucleated red blood cell (NRBC) were purified from the BM and/or PB. Colony-forming unit (CFU) derived from CD34 + BM cells cultured in semi-solid media, and mature red blood cell (RBC) and reticulocytes were enriched via depleting nucleated cells from the PB. DNA and/or RNA were extracted for barcode PCR, high-throughput sequencing, and custom data analysis.
Figure Legend Snippet: Experimental design. Oligonucleotides consisting of a 6bp library ID followed by a 27-35bp high diversity random sequence barcode were inserted into a lentiviral vector flanked by polymerase chain reaction (PCR) primer sites. RM CD34 + hematopoietic stem and progenitor cell (HSPC) were mobilized into the peripheral blood (PB), collected by apheresis, enriched via immunoselection, transduced with the barcoded lentiviral library, and infused back into the total body irradiation (TBI) irradiated autologous macaque. After engraftment, PB and bone marrow (BM) samples were obtained, and various hematopoietic lineages were purified for barcode retrieval and analyses. Lineage cells and nucleated red blood cell (NRBC) were purified from the BM and/or PB. Colony-forming unit (CFU) derived from CD34 + BM cells cultured in semi-solid media, and mature red blood cell (RBC) and reticulocytes were enriched via depleting nucleated cells from the PB. DNA and/or RNA were extracted for barcode PCR, high-throughput sequencing, and custom data analysis.

Techniques Used: Sequencing, Plasmid Preparation, Polymerase Chain Reaction, Transduction, Irradiation, Purification, Derivative Assay, Cell Culture, Next-Generation Sequencing

Clonal relationships between erythroid and other hematopoietic lineages. (A) CD45–CD71+ nucleated red blood cell (NRBC) were FACS-purified from six bone marrow (BM) mononuclear cell (MNC) samples obtained from four macaques post transplantation. A representative flow plot and corresponding cytospins of Wright-Giemsa staining and Benzidine staining of purified NRBC are shown. The percentage of NRBC was 94.3±0.8% by Wright-Giemsa staining, and 95.2% by Benzidine staining, counting at least 500 cells. (B) Heat maps representing the log fractional contributions of the top ten most abundant contributing clones retrieved from each different bone marrow (BM) cell lineage population plotted over all BM cell populations. Each individual row represents the fractional contributions from an individual barcode (clone), and each individual column represents a sample. *A barcode is one of the top ten contributing clones in that cell sample (column). Since the top ten barcoded clones in each sample are plotted across all samples, each row in the heat map has at least one*, and each column has exactly ten*. The rows are ordered by unsupervised hierarchical clustering using Euclidean distances to group barcoded clones together that manifest similar patterns of clonal contributions. The color scale on the right depicts the log fractional contribution size. Samples include CD34 + hematopoietic stem and progenitor cell (HSPC), T cells, B cells, monocytes (Mono), granulocytes (Gr), and NRBC. (C) Pearson correlation coefficients comparing all barcoded clonal contributions between different lineages in the same six BM samples shown in (A). The color scale bar for r values is on the right, the shape and the color signify the strength of the correlation. (D) Stacked histograms displaying the degree of erythroid lineage bias and relative size of barcoded clonal contributions (largest to smallest, with smallest clonal contributions appearing only as overlapping lines). Erythroid lineage bias is calculated via a ratio of the fractional contribution of a barcode to NRBC versus the fractional contribution of the barcode to another lineage (Gr, Mono, CD34 + , T, or B), using the largest fractional contribution among the other lineages for each barcode to calculate the ratio. The positive sign (+) indicates bias towards the NRBC lineage and the negative sign (-) indicates bias away from the NRBC lineage.
Figure Legend Snippet: Clonal relationships between erythroid and other hematopoietic lineages. (A) CD45–CD71+ nucleated red blood cell (NRBC) were FACS-purified from six bone marrow (BM) mononuclear cell (MNC) samples obtained from four macaques post transplantation. A representative flow plot and corresponding cytospins of Wright-Giemsa staining and Benzidine staining of purified NRBC are shown. The percentage of NRBC was 94.3±0.8% by Wright-Giemsa staining, and 95.2% by Benzidine staining, counting at least 500 cells. (B) Heat maps representing the log fractional contributions of the top ten most abundant contributing clones retrieved from each different bone marrow (BM) cell lineage population plotted over all BM cell populations. Each individual row represents the fractional contributions from an individual barcode (clone), and each individual column represents a sample. *A barcode is one of the top ten contributing clones in that cell sample (column). Since the top ten barcoded clones in each sample are plotted across all samples, each row in the heat map has at least one*, and each column has exactly ten*. The rows are ordered by unsupervised hierarchical clustering using Euclidean distances to group barcoded clones together that manifest similar patterns of clonal contributions. The color scale on the right depicts the log fractional contribution size. Samples include CD34 + hematopoietic stem and progenitor cell (HSPC), T cells, B cells, monocytes (Mono), granulocytes (Gr), and NRBC. (C) Pearson correlation coefficients comparing all barcoded clonal contributions between different lineages in the same six BM samples shown in (A). The color scale bar for r values is on the right, the shape and the color signify the strength of the correlation. (D) Stacked histograms displaying the degree of erythroid lineage bias and relative size of barcoded clonal contributions (largest to smallest, with smallest clonal contributions appearing only as overlapping lines). Erythroid lineage bias is calculated via a ratio of the fractional contribution of a barcode to NRBC versus the fractional contribution of the barcode to another lineage (Gr, Mono, CD34 + , T, or B), using the largest fractional contribution among the other lineages for each barcode to calculate the ratio. The positive sign (+) indicates bias towards the NRBC lineage and the negative sign (-) indicates bias away from the NRBC lineage.

Techniques Used: FACS, Purification, Transplantation Assay, Staining, Clone Assay

37) Product Images from "A sensitive repeat identification framework based on short and long reads"

Article Title: A sensitive repeat identification framework based on short and long reads

Journal: Nucleic Acids Research

doi: 10.1093/nar/gkab563

Comparison between the size distribution range of the detected fragments generated from the five detection modes of LongRepMarker on 21 groups of real datasets and the size distribution range of the detected fragments of benchmarking methods. For the hybrid mode (i.e. NGS short reads + barcode linked/SMS reads), since there is no existing methods take the same type of inputs, we only compared the two modes of LongRepMarker.
Figure Legend Snippet: Comparison between the size distribution range of the detected fragments generated from the five detection modes of LongRepMarker on 21 groups of real datasets and the size distribution range of the detected fragments of benchmarking methods. For the hybrid mode (i.e. NGS short reads + barcode linked/SMS reads), since there is no existing methods take the same type of inputs, we only compared the two modes of LongRepMarker.

Techniques Used: Generated, Next-Generation Sequencing

The pipeline of LongRepMarker. ( A ) shows five working modes of LongRepMarker, which are reference-assisted mode, de novo mode based on only NGS short paired-end reads, de novo mode based on NGS short paired-end reads + barcode linked reads, de novo mode based on NGS short paired-end reads + SMS long reads and de novo mode based on only SMS long reads. ( B ) shows the principle of finding overlaps between chromosomes and contigs by using minimap2. ( C ) Transforming overlaps into unique k-mers by DSK. ( D ) Using minimap2 to obtain multi-alignment unique k-mers and the regions on chromosomes and contigs that can be covered by these unique k-mers . ( E ) Using minimap2 to obtain multi-alignment regions and single-alignment regions on chromosomes, contigs and long reads, and the sequences marked in multi-alignment regions are saved in the final repeat library. ( F ) Single-alignment regions are cut into several smaller segments, and some multi-alignment segments are saved in the final repeat library. ( G ) Analyzing the relationship and spacing between these saved sequences, combining some saved sequences and their gaps that meet certain conditions to a complete fragment and replacing the corresponding saved sequences in the final detection results by this fragment. ( H ) Components of the final repeat library. (A), (B), (B1), (C1), (D1), (F), (G) and (H) illustrate the workflow of the detection mode based on only the SMS long reads.
Figure Legend Snippet: The pipeline of LongRepMarker. ( A ) shows five working modes of LongRepMarker, which are reference-assisted mode, de novo mode based on only NGS short paired-end reads, de novo mode based on NGS short paired-end reads + barcode linked reads, de novo mode based on NGS short paired-end reads + SMS long reads and de novo mode based on only SMS long reads. ( B ) shows the principle of finding overlaps between chromosomes and contigs by using minimap2. ( C ) Transforming overlaps into unique k-mers by DSK. ( D ) Using minimap2 to obtain multi-alignment unique k-mers and the regions on chromosomes and contigs that can be covered by these unique k-mers . ( E ) Using minimap2 to obtain multi-alignment regions and single-alignment regions on chromosomes, contigs and long reads, and the sequences marked in multi-alignment regions are saved in the final repeat library. ( F ) Single-alignment regions are cut into several smaller segments, and some multi-alignment segments are saved in the final repeat library. ( G ) Analyzing the relationship and spacing between these saved sequences, combining some saved sequences and their gaps that meet certain conditions to a complete fragment and replacing the corresponding saved sequences in the final detection results by this fragment. ( H ) Components of the final repeat library. (A), (B), (B1), (C1), (D1), (F), (G) and (H) illustrate the workflow of the detection mode based on only the SMS long reads.

Techniques Used: Next-Generation Sequencing

Comparison between the detection results generated from the de novo mode of LongRepMarker based on three groups of NGS short reads + barcode linked reads (HG004_NA24143_father, HG004_NA24143_mother and HG002_NA24385_son) and the detection results generated from the de novo mode of LongRepMarker based on three groups of NGS short reads + SMS long reads (CCS) in terms of the proportion of covering the human RepBase library and the repetitive regions on the reference genome of human. The label All represents the total coverage ratio, which is the sum of the proportion of detection results covering all kinds of repetitive sequences in the corresponding library. The label DNA indicates the proportion of the detection results covering the DNA transposon elements-type repetitive sequences in the corresponding library, label LINEs indicates the proportion of the detection results covering the LINEs-type repetitive sequences in the corresponding library, label LTR indicates the proportion of the detection results covering the LTR-type repetitive sequences in the corresponding library, label lc indicates the proportion of the detection results covering the low complexity-type repetitive sequences in the corresponding library, and label Satellite indicates the proportion of the detection results covering the Satellite-type repetitive sequences in the corresponding library. Sub-figures ( A ) to ( C ) show the comparison of the ratio of the detection results of these two models on the three groups of hybrid sequencing data covering the human RepBase library. Sub-figures ( D ) to ( F ) show the comparison of the ratio of the detection results of these two models on the three groups of hybrid sequencing data covering the repetitive sequences on the reference genome of human.
Figure Legend Snippet: Comparison between the detection results generated from the de novo mode of LongRepMarker based on three groups of NGS short reads + barcode linked reads (HG004_NA24143_father, HG004_NA24143_mother and HG002_NA24385_son) and the detection results generated from the de novo mode of LongRepMarker based on three groups of NGS short reads + SMS long reads (CCS) in terms of the proportion of covering the human RepBase library and the repetitive regions on the reference genome of human. The label All represents the total coverage ratio, which is the sum of the proportion of detection results covering all kinds of repetitive sequences in the corresponding library. The label DNA indicates the proportion of the detection results covering the DNA transposon elements-type repetitive sequences in the corresponding library, label LINEs indicates the proportion of the detection results covering the LINEs-type repetitive sequences in the corresponding library, label LTR indicates the proportion of the detection results covering the LTR-type repetitive sequences in the corresponding library, label lc indicates the proportion of the detection results covering the low complexity-type repetitive sequences in the corresponding library, and label Satellite indicates the proportion of the detection results covering the Satellite-type repetitive sequences in the corresponding library. Sub-figures ( A ) to ( C ) show the comparison of the ratio of the detection results of these two models on the three groups of hybrid sequencing data covering the human RepBase library. Sub-figures ( D ) to ( F ) show the comparison of the ratio of the detection results of these two models on the three groups of hybrid sequencing data covering the repetitive sequences on the reference genome of human.

Techniques Used: Generated, Next-Generation Sequencing, Sequencing

38) Product Images from "FLEXBAR--Flexible Barcode and Adapter Processing for Next-Generation Sequencing Platforms"

Article Title: FLEXBAR--Flexible Barcode and Adapter Processing for Next-Generation Sequencing Platforms

Journal: Biology

doi: 10.3390/biology1030895

Total number of identified splice leader sequences (light gray bars) and number of estimated false discoveries (black bars) for SL1 and SL2 in data set SRR353594. The following parameters were varied: --barcode-min-overlap {10,15,20}, --barcode-threshold {0,1,2} and --barcode-gap-cost was set to -100. All reads were either assigned to SL1 or SL2 if they passed the alignment criteria (preference is given to SL1 in case of equally scoring alignments). The ratio of all discoveries versus false discoveries is highest for the {20,0} parameter set.
Figure Legend Snippet: Total number of identified splice leader sequences (light gray bars) and number of estimated false discoveries (black bars) for SL1 and SL2 in data set SRR353594. The following parameters were varied: --barcode-min-overlap {10,15,20}, --barcode-threshold {0,1,2} and --barcode-gap-cost was set to -100. All reads were either assigned to SL1 or SL2 if they passed the alignment criteria (preference is given to SL1 in case of equally scoring alignments). The ratio of all discoveries versus false discoveries is highest for the {20,0} parameter set.

Techniques Used:

39) Product Images from "Deep sequencing of nonenzymatic RNA primer extension"

Article Title: Deep sequencing of nonenzymatic RNA primer extension

Journal: bioRxiv

doi: 10.1101/2020.02.18.955120

Data Analysis. A. Cartoon of a hairpin construct after R T Handle ligation. The labels and color coding indicate the various sequence regions used during data processing. The hairpin and handles are defined sequences (they are “fixed”), the Prefix is a four-base motif with the two caged bases, the Template is of defined length but the analysis does not specify a defined sequence (so we can analyse randomised templates), and the Product is of indeterminate length and sequence. B. The double-stranded DNA generated by barcoding PCR (the barcode is to the 3’ of Fix 2 and not shown). The final location of each region from the construct is labelled and color-coded. Paired-end sequencing provides both the forward (R1) and reverse (R2) sequences, which can be compared against each other for quality control. A series of checks identifies the fixed sequences, filters out low-quality reads, and extracts the Template and Product. C. The end result of Pre-processing is a set of Template-Product pairs. Unextended bases are indicated by an asterisk, and a placeholding A (part of the Prefix motif, immediately prior to the first caged base) is included as an internal marker. D. Template-Product pairs are queried in the Characterise stage to assay the sequence properties of templates and complementary products, and indicate the position and context of mismatches.
Figure Legend Snippet: Data Analysis. A. Cartoon of a hairpin construct after R T Handle ligation. The labels and color coding indicate the various sequence regions used during data processing. The hairpin and handles are defined sequences (they are “fixed”), the Prefix is a four-base motif with the two caged bases, the Template is of defined length but the analysis does not specify a defined sequence (so we can analyse randomised templates), and the Product is of indeterminate length and sequence. B. The double-stranded DNA generated by barcoding PCR (the barcode is to the 3’ of Fix 2 and not shown). The final location of each region from the construct is labelled and color-coded. Paired-end sequencing provides both the forward (R1) and reverse (R2) sequences, which can be compared against each other for quality control. A series of checks identifies the fixed sequences, filters out low-quality reads, and extracts the Template and Product. C. The end result of Pre-processing is a set of Template-Product pairs. Unextended bases are indicated by an asterisk, and a placeholding A (part of the Prefix motif, immediately prior to the first caged base) is included as an internal marker. D. Template-Product pairs are queried in the Characterise stage to assay the sequence properties of templates and complementary products, and indicate the position and context of mismatches.

Techniques Used: Construct, Ligation, Sequencing, Generated, Polymerase Chain Reaction, Marker

Protocol for Preparing RNA Hairpin Constructs for Sequencing. A. NERPE-Seq RNA hairpin constructs contain a hairpin loop that physically connects the template sequence to the primer so that both the product of nonenzymatic primer extension and the corresponding template are on one continuous RNA strand and can therefore be sequenced together. Two caged bases (magenta Xs) prevent primer extension from encroaching on the downstream 5’ Handle. The 5’ Handle Block is complementary to the 5’ Handle and prevents it from interfering with primer extension. B. The primer extension reaction is quenched with a desalting size-exclusion spin column, the caged bases are uncaged and the target RNA is further gel purified. C-D. The pre-adenylated DNA RT Handle (blocked on its 3’ end to prevent self-ligation) is ligated to the 3’ end of the RNA hairpin (the site of primer extension). E. The ligase is removed by a Proteinase K digestion, the target RNA-DNA is phenol-chloroform extracted, and the reverse transcription primer is annealed to the RT Handle. F-G. Reverse transcription generates the cDNA; the RNA is degraded, and the cDNA is isolated with a spin column. The Region of Interest (ROI) harbours the template, hairpin and any product sequences. H. PCR is used to barcode the DNA and add additional required flanking sequences. Each barcode identifies DNA from a specific experiment and enables the sequencing of samples drawn from multiple experimental conditions at the same time. I. The target PCR products are purified, and validated by automated electrophoresis and quantitative PCR prior to sequencing.
Figure Legend Snippet: Protocol for Preparing RNA Hairpin Constructs for Sequencing. A. NERPE-Seq RNA hairpin constructs contain a hairpin loop that physically connects the template sequence to the primer so that both the product of nonenzymatic primer extension and the corresponding template are on one continuous RNA strand and can therefore be sequenced together. Two caged bases (magenta Xs) prevent primer extension from encroaching on the downstream 5’ Handle. The 5’ Handle Block is complementary to the 5’ Handle and prevents it from interfering with primer extension. B. The primer extension reaction is quenched with a desalting size-exclusion spin column, the caged bases are uncaged and the target RNA is further gel purified. C-D. The pre-adenylated DNA RT Handle (blocked on its 3’ end to prevent self-ligation) is ligated to the 3’ end of the RNA hairpin (the site of primer extension). E. The ligase is removed by a Proteinase K digestion, the target RNA-DNA is phenol-chloroform extracted, and the reverse transcription primer is annealed to the RT Handle. F-G. Reverse transcription generates the cDNA; the RNA is degraded, and the cDNA is isolated with a spin column. The Region of Interest (ROI) harbours the template, hairpin and any product sequences. H. PCR is used to barcode the DNA and add additional required flanking sequences. Each barcode identifies DNA from a specific experiment and enables the sequencing of samples drawn from multiple experimental conditions at the same time. I. The target PCR products are purified, and validated by automated electrophoresis and quantitative PCR prior to sequencing.

Techniques Used: Construct, Sequencing, Blocking Assay, Purification, Ligation, Isolation, Polymerase Chain Reaction, Electrophoresis, Real-time Polymerase Chain Reaction

40) Product Images from "Clonal barcoding with qPCR detection enables live cell functional analyses for cancer research"

Article Title: Clonal barcoding with qPCR detection enables live cell functional analyses for cancer research

Journal: Nature Communications

doi: 10.1038/s41467-022-31536-5

SunCatcher enables identification and quantification of early spontaneous metastasis. a Calibration curves were generated for indicated tissues by serially diluting known amounts of barcoded tumor cell gDNA into a fixed amount of normal tissue gDNA. From left to right: lung, long bones (from femur and tibia), mandible. b 2.5 × 10 5 barcoded Met1 tumor cells were injected bilaterally into the mammary fat pads ( n = 5 animals) and tumors were allowed to grow for 21 days, at which point tissues were harvested and metastasis burden was calculated. Dashed lines indicate the background signals from each indicated tissue type. Tissues with signal above the background were considered positive for metastasis and estimated tumor cell number per 0.1 mg tissue was calculated based on the calibration curve for that specific tissue. c Barcode composition analysis on tissues with positive metastasis signal. Bars represent percent of total barcode signal (100%) within each sample. Also shown are mouse identities, total primary tumor burden for each animal, and estimated numbers of metastases per tissue; N.D., not detected. Source data are provided as a Source Data file.
Figure Legend Snippet: SunCatcher enables identification and quantification of early spontaneous metastasis. a Calibration curves were generated for indicated tissues by serially diluting known amounts of barcoded tumor cell gDNA into a fixed amount of normal tissue gDNA. From left to right: lung, long bones (from femur and tibia), mandible. b 2.5 × 10 5 barcoded Met1 tumor cells were injected bilaterally into the mammary fat pads ( n = 5 animals) and tumors were allowed to grow for 21 days, at which point tissues were harvested and metastasis burden was calculated. Dashed lines indicate the background signals from each indicated tissue type. Tissues with signal above the background were considered positive for metastasis and estimated tumor cell number per 0.1 mg tissue was calculated based on the calibration curve for that specific tissue. c Barcode composition analysis on tissues with positive metastasis signal. Bars represent percent of total barcode signal (100%) within each sample. Also shown are mouse identities, total primary tumor burden for each animal, and estimated numbers of metastases per tissue; N.D., not detected. Source data are provided as a Source Data file.

Techniques Used: Generated, Injection

SunCatcher clonal barcoding and functional analyses. a SunCatcher utilizes two rounds of single cell cloning to ensure that each subclone has only 1 unique barcode and that each cell within that subclone contains the same barcode insertion site. Due to the single cell cloning approach, custom BC pools of any combination can be designed. (FACS, fluorescence-activated cell sorting; NBC, non-barcoded clone; BPP, barcoded polyclonal population (polyclonal for barcode insertion site); BC, barcoded clone; BC Pool, population containing multiple BCs). b BCs or BC Pools (input) can be entered into any experiment and detected at end point (output) using various deconvolution approaches. Deconvolution by quantitative polymerase chain reaction (qPCR) provides a cost-effective, rapid, and highly sensitive method for detecting and quantifying BCs. SunCatcher enables retrieval of all clones, including those negatively selected during experimentation, for further analyses and/or design and testing of custom BC Pools.
Figure Legend Snippet: SunCatcher clonal barcoding and functional analyses. a SunCatcher utilizes two rounds of single cell cloning to ensure that each subclone has only 1 unique barcode and that each cell within that subclone contains the same barcode insertion site. Due to the single cell cloning approach, custom BC pools of any combination can be designed. (FACS, fluorescence-activated cell sorting; NBC, non-barcoded clone; BPP, barcoded polyclonal population (polyclonal for barcode insertion site); BC, barcoded clone; BC Pool, population containing multiple BCs). b BCs or BC Pools (input) can be entered into any experiment and detected at end point (output) using various deconvolution approaches. Deconvolution by quantitative polymerase chain reaction (qPCR) provides a cost-effective, rapid, and highly sensitive method for detecting and quantifying BCs. SunCatcher enables retrieval of all clones, including those negatively selected during experimentation, for further analyses and/or design and testing of custom BC Pools.

Techniques Used: Functional Assay, Clone Assay, FACS, Fluorescence, Real-time Polymerase Chain Reaction

BC Detection by qPCR and next generation sequencing methods. a qPCR-based BC identification and quantification is achieved by subjecting sample gDNA to 2 rounds of PCR using pre-amplification primers (red, round 1) and BC-specific primers (black, round 2). b Heatmap showing hamming distance between all barcodes in the Met1 BC Pool. c Heatmap of qPCR cycle threshold (CT) values after testing each indicated barcode oligonucleotide primer against every Met1 BC population and the Met1-BC pool. d Multiplexing of gDNA samples for NGS is achieved in 3 steps. 1: Barcode regions in each gDNA sample are PCR-amplified using primers to universal barcode flanking sequences. To the resulting amplicons from each sample, one of 20 available unique 8-bp indexes is added downstream (3' end) of the barcode region via PCR. 2: Up to 20 different indexed amplicons (various colors) are pooled to generate a single barcode-index library. 3: Amplicons in each barcode-index library are ligated to Illumina adaptors. A universal P5 adaptor (green) is ligated upstream (5' end) and one of 24 available P7 adaptors (e.g., TruSeq1, blue and Truseq2, orange) is ligated downstream (3' end) of the barcode region. e Sequencing read counts from 2 HMLER-HR BC test samples. Each library corresponds to a single Illumina adaptor, and the expected barcode pair for each library is indicated. For each library, the average false-positive read count (±S.E.M.) per BC is shown for BCs in the HMLER-HR BC Pool (black) and for BCs not represented in the HMLER-HR BC Pool (red). All BCs that yielded a read count are represented. f Example of thresholding method for identifying BCs from experimental samples by NGS. Graph shows read counts for each barcode from an HMLER-HR BC Pool tumor. The only false positive read corresponds to BC43, which was used to set the false-positive threshold (red line). Barcodes with read counts below the threshold were discarded as sequencer noise. Source data are provided as a Source Data file.
Figure Legend Snippet: BC Detection by qPCR and next generation sequencing methods. a qPCR-based BC identification and quantification is achieved by subjecting sample gDNA to 2 rounds of PCR using pre-amplification primers (red, round 1) and BC-specific primers (black, round 2). b Heatmap showing hamming distance between all barcodes in the Met1 BC Pool. c Heatmap of qPCR cycle threshold (CT) values after testing each indicated barcode oligonucleotide primer against every Met1 BC population and the Met1-BC pool. d Multiplexing of gDNA samples for NGS is achieved in 3 steps. 1: Barcode regions in each gDNA sample are PCR-amplified using primers to universal barcode flanking sequences. To the resulting amplicons from each sample, one of 20 available unique 8-bp indexes is added downstream (3' end) of the barcode region via PCR. 2: Up to 20 different indexed amplicons (various colors) are pooled to generate a single barcode-index library. 3: Amplicons in each barcode-index library are ligated to Illumina adaptors. A universal P5 adaptor (green) is ligated upstream (5' end) and one of 24 available P7 adaptors (e.g., TruSeq1, blue and Truseq2, orange) is ligated downstream (3' end) of the barcode region. e Sequencing read counts from 2 HMLER-HR BC test samples. Each library corresponds to a single Illumina adaptor, and the expected barcode pair for each library is indicated. For each library, the average false-positive read count (±S.E.M.) per BC is shown for BCs in the HMLER-HR BC Pool (black) and for BCs not represented in the HMLER-HR BC Pool (red). All BCs that yielded a read count are represented. f Example of thresholding method for identifying BCs from experimental samples by NGS. Graph shows read counts for each barcode from an HMLER-HR BC Pool tumor. The only false positive read corresponds to BC43, which was used to set the false-positive threshold (red line). Barcodes with read counts below the threshold were discarded as sequencer noise. Source data are provided as a Source Data file.

Techniques Used: Real-time Polymerase Chain Reaction, Next-Generation Sequencing, Polymerase Chain Reaction, Amplification, Multiplexing, Sequencing

SunCatcher approach maintains tumorigenic properties and enables analysis of heterogeneity. a Phase contrast images of indicated Met1 BCs; scale bar = 100 μm. Images are representative of 2 independent observations. b Sand plot showing clonal composition (cumulative percentage) of Met1 BC Pool over 7 days (d) (2 passages) in vitro. c Growth of Met1 Parental (black; Y = 0.3360x + 0.1699) and Met1 BC Pool (gray; Y = 0.3562x + 0.4121) over 8 days in culture; n = 4 replicates per group; data are presented as mean values ± SD. d Growth of tumors from Met1 parental cells ( n = 8 tumors) and Met1 BC Pool cells ( n = 9 tumors) in FVB mice; n = 5 mice per cohort, data are presented as mean values ± SEM. e , f Mass (grams) of tumors ( e ) and spleens ( f ) from mice in experiment represented in ( d ); data are presented as mean values ± SEM. g Quantitative PCR assessment of barcode composition in the Met1 BC Pool at time of injection, in each of ten tumors ( n = 5 mice) after 18 days, and average composition of all tumors in the cohort. Bars show indicated barcodes as a percent of total barcode signal (100%) within each sample; tumor mass is indicated above each bar. Key shows color code for each BC. h , i Comparison of the average representation of each BC in tumors for each of two independent experiments ( n = 8 tumors for experiment 1; n = 6 tumors for experiment 2). BCs that constituted > 0.5% of total BC signal ( h ) and
Figure Legend Snippet: SunCatcher approach maintains tumorigenic properties and enables analysis of heterogeneity. a Phase contrast images of indicated Met1 BCs; scale bar = 100 μm. Images are representative of 2 independent observations. b Sand plot showing clonal composition (cumulative percentage) of Met1 BC Pool over 7 days (d) (2 passages) in vitro. c Growth of Met1 Parental (black; Y = 0.3360x + 0.1699) and Met1 BC Pool (gray; Y = 0.3562x + 0.4121) over 8 days in culture; n = 4 replicates per group; data are presented as mean values ± SD. d Growth of tumors from Met1 parental cells ( n = 8 tumors) and Met1 BC Pool cells ( n = 9 tumors) in FVB mice; n = 5 mice per cohort, data are presented as mean values ± SEM. e , f Mass (grams) of tumors ( e ) and spleens ( f ) from mice in experiment represented in ( d ); data are presented as mean values ± SEM. g Quantitative PCR assessment of barcode composition in the Met1 BC Pool at time of injection, in each of ten tumors ( n = 5 mice) after 18 days, and average composition of all tumors in the cohort. Bars show indicated barcodes as a percent of total barcode signal (100%) within each sample; tumor mass is indicated above each bar. Key shows color code for each BC. h , i Comparison of the average representation of each BC in tumors for each of two independent experiments ( n = 8 tumors for experiment 1; n = 6 tumors for experiment 2). BCs that constituted > 0.5% of total BC signal ( h ) and

Techniques Used: In Vitro, Mouse Assay, Real-time Polymerase Chain Reaction, Injection

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    Illumina Inc barcodes
    Flowchart for generating MinION <t>barcodes</t> from experimental set-up to final barcodes. The novel steps introduced in this study are highlighted in green and the scripts available in miniBarcoder for analyses are further indicated.
    Barcodes, supplied by Illumina Inc, used in various techniques. Bioz Stars score: 88/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Illumina Inc barcode
    Comparative schematic of small RNA barcoding methods. The three methods start with ligation of a 3' and 5' RNA adapter to generate a substrate for RT-PCR. In the pre-PCR barcoding method, the <t>barcode</t> is incorporated in the 5' adapter. In the TruSeq method, the barcode is incorporated in one of the RT-PCR primers. In the PALM barcoding method, the amplified RT-PCR product is A-tailed and ligated to a T-tailed barcoded adapter.
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    illumina inc cell barcode assignment
    Sicelore and ScNapBar CPU time comparison. ( A ) ScNapBar CPU time depends on the number of whitelist barcodes (allowing an edit distance of > 2 and and offset of up to 4 bp between adapter and <t>barcode).</t> Gray area represents the standard deviation for 10 runs. ( B ) Comparison of ScNapBar and Sicelore CPU times. Benchmark was measured using one million barcode sequences and 2052 barcodes in the whitelist.
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    Illumina Inc potential barcode combinations
    Nanoparticles co-formulated with siRNA and a DNA <t>barcode</t> can be used to readout quantify how > 100 different LNPs functionally deliver RNA into the cytoplasm of target cells in a single mouse. (A) Unlike previous biodistribution screens, which cannot distinguish between bound particles, particles stuck in endosomes, and particles that delivered RNA into the cytoplasm, our method identifies LNPs that functionally deliver siRNA. We do so by isolating cells that are ICAM Low and sequencing barcodes in those cells. (B,C) ICAM-2 protein expression in lung endothelial cells after mice were treated with 7C1 carrying a barcode and either siLuc or siICAM-2. ICAM-2 protein expression decreased in a dose-dependent manner. (D) siRNA-mediated silencing also led to a dose-dependent increase in ICAM Low lung endothelial cells.
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    Image Search Results


    Flowchart for generating MinION barcodes from experimental set-up to final barcodes. The novel steps introduced in this study are highlighted in green and the scripts available in miniBarcoder for analyses are further indicated.

    Journal: bioRxiv

    Article Title: Rapid, large-scale species discovery in hyperdiverse taxa using 1D MinION sequencing

    doi: 10.1101/622365

    Figure Lengend Snippet: Flowchart for generating MinION barcodes from experimental set-up to final barcodes. The novel steps introduced in this study are highlighted in green and the scripts available in miniBarcoder for analyses are further indicated.

    Article Snippet: Indeed, even the initial estimates of barcodes (“MAFFT” & “RACON”) have very high accuracy ( > 99.5%) when compared to Illumina data, while the accuracy of consolidated barcodes is even higher ( > 99.9%).

    Techniques:

    Ambiguities in MAFFT+AA (Purple), RACON+AA (Yellow) and Consolidated barcodes (Green) with varying namino parameters (1,2 and 3). One outlier value for Racon+3AA barcode was excluded from the plot. The plot shows that the consolidated barcodes have few ambiguities remaining.

    Journal: bioRxiv

    Article Title: Rapid, large-scale species discovery in hyperdiverse taxa using 1D MinION sequencing

    doi: 10.1101/622365

    Figure Lengend Snippet: Ambiguities in MAFFT+AA (Purple), RACON+AA (Yellow) and Consolidated barcodes (Green) with varying namino parameters (1,2 and 3). One outlier value for Racon+3AA barcode was excluded from the plot. The plot shows that the consolidated barcodes have few ambiguities remaining.

    Article Snippet: Indeed, even the initial estimates of barcodes (“MAFFT” & “RACON”) have very high accuracy ( > 99.5%) when compared to Illumina data, while the accuracy of consolidated barcodes is even higher ( > 99.9%).

    Techniques:

    Effect of re-pooling on coverage of barcodes for both sets of specimens. Barcodes with coverage

    Journal: bioRxiv

    Article Title: Rapid, large-scale species discovery in hyperdiverse taxa using 1D MinION sequencing

    doi: 10.1101/622365

    Figure Lengend Snippet: Effect of re-pooling on coverage of barcodes for both sets of specimens. Barcodes with coverage

    Article Snippet: Indeed, even the initial estimates of barcodes (“MAFFT” & “RACON”) have very high accuracy ( > 99.5%) when compared to Illumina data, while the accuracy of consolidated barcodes is even higher ( > 99.9%).

    Techniques:

    Comparative schematic of small RNA barcoding methods. The three methods start with ligation of a 3' and 5' RNA adapter to generate a substrate for RT-PCR. In the pre-PCR barcoding method, the barcode is incorporated in the 5' adapter. In the TruSeq method, the barcode is incorporated in one of the RT-PCR primers. In the PALM barcoding method, the amplified RT-PCR product is A-tailed and ligated to a T-tailed barcoded adapter.

    Journal: PLoS ONE

    Article Title: Quantitative Bias in Illumina TruSeq and a Novel Post Amplification Barcoding Strategy for Multiplexed DNA and Small RNA Deep Sequencing

    doi: 10.1371/journal.pone.0026969

    Figure Lengend Snippet: Comparative schematic of small RNA barcoding methods. The three methods start with ligation of a 3' and 5' RNA adapter to generate a substrate for RT-PCR. In the pre-PCR barcoding method, the barcode is incorporated in the 5' adapter. In the TruSeq method, the barcode is incorporated in one of the RT-PCR primers. In the PALM barcoding method, the amplified RT-PCR product is A-tailed and ligated to a T-tailed barcoded adapter.

    Article Snippet: The adapters used in the protocol were modified to include a barcode and to allow for Illumina index sequencing with the Illumina multiplexing index read sequencing primer.

    Techniques: Ligation, Reverse Transcription Polymerase Chain Reaction, Polymerase Chain Reaction, Amplification

    Sicelore and ScNapBar CPU time comparison. ( A ) ScNapBar CPU time depends on the number of whitelist barcodes (allowing an edit distance of > 2 and and offset of up to 4 bp between adapter and barcode). Gray area represents the standard deviation for 10 runs. ( B ) Comparison of ScNapBar and Sicelore CPU times. Benchmark was measured using one million barcode sequences and 2052 barcodes in the whitelist.

    Journal: RNA

    Article Title: Single-cell transcriptome sequencing on the Nanopore platform with ScNapBar

    doi: 10.1261/rna.078154.120

    Figure Lengend Snippet: Sicelore and ScNapBar CPU time comparison. ( A ) ScNapBar CPU time depends on the number of whitelist barcodes (allowing an edit distance of > 2 and and offset of up to 4 bp between adapter and barcode). Gray area represents the standard deviation for 10 runs. ( B ) Comparison of ScNapBar and Sicelore CPU times. Benchmark was measured using one million barcode sequences and 2052 barcodes in the whitelist.

    Article Snippet: We performed an in silico benchmark of cell barcode assignment when both, cell barcode and UMI, are found in the Nanopore read.

    Techniques: Standard Deviation

    Combined single-cell Illumina and Nanopore sequencing strategy. GFP+/− cells are pooled and sequenced on the Illumina and Nanopore platform. The Nanopore platform generates long cDNA sequencing reads that are used in barcode calling and estimating read error parameters. The Illumina data are used to estimate the total number of cells in sequencing and the represented cell barcodes. The simulated data are then used to parameterize a Bayesian model of barcode alignment features to discriminate correct versus false barcode assignments. This model is then used on the real data to assign cell barcodes to Nanopore reads. The GFP label and known NMD transcripts can be used to validate this assignment.

    Journal: RNA

    Article Title: Single-cell transcriptome sequencing on the Nanopore platform with ScNapBar

    doi: 10.1261/rna.078154.120

    Figure Lengend Snippet: Combined single-cell Illumina and Nanopore sequencing strategy. GFP+/− cells are pooled and sequenced on the Illumina and Nanopore platform. The Nanopore platform generates long cDNA sequencing reads that are used in barcode calling and estimating read error parameters. The Illumina data are used to estimate the total number of cells in sequencing and the represented cell barcodes. The simulated data are then used to parameterize a Bayesian model of barcode alignment features to discriminate correct versus false barcode assignments. This model is then used on the real data to assign cell barcodes to Nanopore reads. The GFP label and known NMD transcripts can be used to validate this assignment.

    Article Snippet: We performed an in silico benchmark of cell barcode assignment when both, cell barcode and UMI, are found in the Nanopore read.

    Techniques: Nanopore Sequencing, Sequencing

    Number of the Nanopore reads identified by ScNapBar and Sicelore at each processing step. We inspected each processing step on real data (low lllumina saturation of 11.3%). The first two steps are identical for both workflows. Total Reads: Number of input reads, aligned to genome: Number of reads aligned to genome. The next three steps are workflow-specific: Aligned to adapter: Number of reads with identified adapter sequence, aligned to barcode: Number of reads with aligned barcode sequence, Assigned to barcode: Number of predictions by each workflow. The last step is a validation of the previous assignment step after additional Illumina sequencing, which increases the Illumina saturation to 52%, and using UMI matches, see main text.

    Journal: RNA

    Article Title: Single-cell transcriptome sequencing on the Nanopore platform with ScNapBar

    doi: 10.1261/rna.078154.120

    Figure Lengend Snippet: Number of the Nanopore reads identified by ScNapBar and Sicelore at each processing step. We inspected each processing step on real data (low lllumina saturation of 11.3%). The first two steps are identical for both workflows. Total Reads: Number of input reads, aligned to genome: Number of reads aligned to genome. The next three steps are workflow-specific: Aligned to adapter: Number of reads with identified adapter sequence, aligned to barcode: Number of reads with aligned barcode sequence, Assigned to barcode: Number of predictions by each workflow. The last step is a validation of the previous assignment step after additional Illumina sequencing, which increases the Illumina saturation to 52%, and using UMI matches, see main text.

    Article Snippet: We performed an in silico benchmark of cell barcode assignment when both, cell barcode and UMI, are found in the Nanopore read.

    Techniques: Sequencing

    Sensitivity and specificity of ScNapBar and Sicelore on 100 Illumina libraries with different levels of saturation. ( A ) Barcode assignment with UMI matches. ( B ) Barcode assignment without UMI matches (ScNapBar score > 50). ( C ) Benchmark of the specificity and sensitivity of the Illumina library with 100% saturation. We compared the barcode assignments with ScNapBar score > 1–99, and the assignments from Sicelore with UMI support are roughly equivalent to the ScNapBar score > 90.

    Journal: RNA

    Article Title: Single-cell transcriptome sequencing on the Nanopore platform with ScNapBar

    doi: 10.1261/rna.078154.120

    Figure Lengend Snippet: Sensitivity and specificity of ScNapBar and Sicelore on 100 Illumina libraries with different levels of saturation. ( A ) Barcode assignment with UMI matches. ( B ) Barcode assignment without UMI matches (ScNapBar score > 50). ( C ) Benchmark of the specificity and sensitivity of the Illumina library with 100% saturation. We compared the barcode assignments with ScNapBar score > 1–99, and the assignments from Sicelore with UMI support are roughly equivalent to the ScNapBar score > 90.

    Article Snippet: We performed an in silico benchmark of cell barcode assignment when both, cell barcode and UMI, are found in the Nanopore read.

    Techniques:

    Nanoparticles co-formulated with siRNA and a DNA barcode can be used to readout quantify how > 100 different LNPs functionally deliver RNA into the cytoplasm of target cells in a single mouse. (A) Unlike previous biodistribution screens, which cannot distinguish between bound particles, particles stuck in endosomes, and particles that delivered RNA into the cytoplasm, our method identifies LNPs that functionally deliver siRNA. We do so by isolating cells that are ICAM Low and sequencing barcodes in those cells. (B,C) ICAM-2 protein expression in lung endothelial cells after mice were treated with 7C1 carrying a barcode and either siLuc or siICAM-2. ICAM-2 protein expression decreased in a dose-dependent manner. (D) siRNA-mediated silencing also led to a dose-dependent increase in ICAM Low lung endothelial cells.

    Journal: Journal of the American Chemical Society

    Article Title: Nanoparticles that deliver RNA to bone marrow identified by in vivo directed evolution

    doi: 10.1021/jacs.8b08976

    Figure Lengend Snippet: Nanoparticles co-formulated with siRNA and a DNA barcode can be used to readout quantify how > 100 different LNPs functionally deliver RNA into the cytoplasm of target cells in a single mouse. (A) Unlike previous biodistribution screens, which cannot distinguish between bound particles, particles stuck in endosomes, and particles that delivered RNA into the cytoplasm, our method identifies LNPs that functionally deliver siRNA. We do so by isolating cells that are ICAM Low and sequencing barcodes in those cells. (B,C) ICAM-2 protein expression in lung endothelial cells after mice were treated with 7C1 carrying a barcode and either siLuc or siICAM-2. ICAM-2 protein expression decreased in a dose-dependent manner. (D) siRNA-mediated silencing also led to a dose-dependent increase in ICAM Low lung endothelial cells.

    Article Snippet: Of the 65,536 (i.e., 48 ) potential barcode combinations, we selected 156 which would work together on Illumina sequencers.

    Techniques: Sequencing, Expressing, Mouse Assay